NoR sponsored projects

A major challenge facing African agriculture is the lack of field boundary (i.e. parcel) maps. Field boundary maps provide the foundations for understanding the characteristics and extents of agricultural systems and how these are changing. This information is essential to organizations that provide services that smallholder farmers need to improve their yields and access to markets, and to adapt to a rapidly changing climate. This project will develop a comprehensive, high-quality set of labels digitized on PlanetScope imagery over Africa intended for training generalizable, regionwide field boundary mapping models, and for refining and validating models for specific regions and years. The labels will be freely available under a Creative Commons license and hosted on Radiant MLHub, from where they will be easily ingested into machine learning pipelines. To date, no such labeled dataset exists, despite the growing interest across the public and private sectors in mapping field boundaries in Africa.

The primary objective of this project is to develop, implement, validate and showcase advanced AI-based methods to automatically map and characterize the spatial extent of slums from Earth Observation (EO) data. This objective is framed and informed by data needs of national and local governments, as well as the civil society, to monitor progress on SDG indicator 11.1.1 on the proportion of the urban population living in slums, informal settlements or inadequate housing. Furthermore, the objective is linked to the information needs of a diverse group of stakeholders that engage in understanding and improving local living conditions.

The objective of this project is to provide training and high-resolution NDVI and NDMI maps to facilitators of the Alito Training Center in Uganda (who manages the Alito Farm) in order to optimize farm inputs, maximize yields, and to promote sustainable agriculture practices.

As part of AGEO-INTERREG project, several Citizens’ Observatory pilots on geohazards (landslides, rockfalls, floods, peat movements, earthquakes, coastal hazards, geotechical risks) are being launched in France, Portugal, Spain, Ireland and UK. The use of EO processed products will be useful to analyze the hazard as a process through the estimation of deformation rates, flooded areas and geomorphological parameters, which will be extremely useful in the risk management tasks. Moreover, AGEO aims to encourage the local use of innovative EO products and services provided by European data infrastructures.

Objectives: Agriculture is one of the few sectors that humanity can not live without, where the climate impact is large (20% of total emissions in Sweden) and furthermore assumed to be difficult to do something about it. However, increased agricultural productivity, i.e. more photosynthesis, results in positive climate effects which IPCC does not fully count. Huge amounts of CO2 are caught by crops that, in turn, generate huge amounts of O2. The yearly agriculture carbon dioxide binding capacity is approximately 15 tons per hectare (crop harvest, straw and roots) The operational understanding of what really happens in a field when crops are growing are clearly lacking in spite of the tremendous amounts of data that modern agriculture equipment is gathering. This results in suboptimal decisions for land use, crop selection, machine usage, fertilization and irrigation for both economic productivity and the climate. Remarkable is that the very detailed harvest data (measurements every fifth second) which have been collected by harvesters for many years is hardly used for operational feedback at least not in Sweden. The project will use AI to quantify limiting agriculture factors, to optimize crop growth in a climate beneficial way and long-term agriculture productivity. Hushållningssällskapets existing

platform markkartering.se will be used for operational usage of the results by the farmers as prescription files for the agriculture equipment.

Extensive field, soil, satellite and sensor data will be used as input to model algorithms driven by Al /ML and spatial analysis (GIS). These algorithms will over time be used to create prescription files to control inputs and other actions in the fields as efficiently and climate friendly as possible. The project objectives are:

#To create a climate positive prescription file model as a decision tool for the next level of precision agriculture. In the end, the result will be presented as prescription files for field equipment, including autonomous vehicles. The results can also be obtained in table form, text or as an interpolated map.

#Expand and enhance Hushållningssällskapet (the Rural Economy and Agricultural Societies) web- based decision support system Markkartering.se (that today has over 2000 active users, about 800 000 hectares) to support as climate friendly and operationally efficient production of food as possible. This will optimize the agricultural climate-affecting factors in multiple ways: #Enhance crop growth and thus bind more carbon into the soil that slowly heals high levels of greenhouse gases;

#Optimize agricultural machine usage and driving;

#Optimize inputs like fertilizers and pesticides;

#New governmental directives that focus on long-term sustainable climate improvements.

The result of our project will be gender neutral however we will try to develop a tool that will change the values of agriculture and in the long-term increase equality both in agriculture and in AI. The project potential of an increased gender balance for farmers is to offer an advanced decision basis that is easy to learn and use for all genders.

As part of the understanding of the hydraulic behavior of the condo river and especially in the lower reach , as well as the protection of islands in this area, this study also aims environmental and economic aspects in the area , as it is already known, the pool Malebo is a strategic area concerning river transport (navigation), irrigation and agriculture, fishing, etc the protection of the morphological degradation of they is also one of the challenges in this work , for this purpose our study aims to master all the scientific information on the hydraulic and hydrological level in order to serve other economic activity and

environmental ,This prompts us to launch the main questions as follows: How can we develop an agent-based simulation model (ABMS) for navigation chart in the lower reach exactly in the stanely pool that includes (hydraulic, hydrological and socio-economic aspects), with this complexity, to find scenario optimal of navigation in the pool male boo , and if we can generate this approach in all the Congo river ? What are the assumptions, approaches and data needed to develop this model?

For my master thesis I am investigating the application of InSAR for glacial lakes in the Himalaya. In order to check the InSAR results I am using optical imagery – sentinel 2, which is why I would like access to the sentinel hub.

Archaeological prospection in Saruq al hadid is of significant interest to find the complete story of prehistoric settlements lived in Dubai, United arab emirates. Located at 50 km in the southeast of Dubai at the north of al Rub’al khali desert, Saruq al Hadid (SA) archaeological site is discovered since 2002. More than 15 000 artifacts have been identified after more than 20 excavations. The location of this site in the middle of desert between the dunes is mysterious because there is no available close freshwater critical for human survival and raw material sources critical for metallurgical industry. The use of remote sensing satellite high resolution radar and multispectral images enhance widely the possibilities of archaeological prospection. This project aims to prescreen potential buried archaeological sites in that desert region. This work is the first attempt made until now in evaluating the detectability of archaeological remains using satellite images data in United Arab Emirates. The outcomes are important to guide and help the excavation missions and the archaeologist for the planning of future excavation campaigns.

At ArchAI, we use ΑΙ to detect archaeological sites on LiDAR and satellite imagery automatically. We have shown the success of this technology with LiDAR data, detecting thousands of previously unknown sites, and our customers include the Forestry Commission and the National Trust. On LiDAR, we specifically look for earthworks (humbs and bumps in the landscape). However, most housing development occurs on farmland where ploughing has levelled earthworks, and satellite imagery is a more reliable source. In addition, archaeology is revealed on satellite imagery in agricultural fields through crop stress revealing sub-soil walls and ditches. Innovations in Satellite imagery have increased the frequency of high-resolution (<1m) information, meaning that a sufficiently trained ΑΙ can now derive archaeology from big data.

Our initial tests on aerial imagery have shown that the results are variable, depending on crops, seasonality, and weather. As such, we require to develop our technology further to account for this and create a higher volume of training data. Satellite imagery has higher temporal revisits, allowing for wider choice and availability of potential training data.

If our research is successful, we will be able to sell these assessments to our customers in the construction industry. It is a legal requirement to consider archaeology before development. However, archaeology is an unknown risk in projects, and it currently takes 6-24 months to reach high accuracy, which involves several stages of fieldwork and even costly excavations. Using satellite imagery and the SentinelHub, our proposed workflow could allow for instant assessment of construction project archaeological risks.

The objective of the project is to focus on sustainable resources in Africa, assessing deforestation in countries like Gabon, Ivory Coast, Uganda etc. This will help in understanding the potential risk of deforestation and high risk areas, so that we can take necessary measures to manage the phenomenon. Moreover, this will help us as a company to attain our sustainable goals for the future.

This research is one of the requirements to complete my studies at the State University of Semarang. The theme I took was the relationship between green open space and the level of thermal comfort, especially in the city centre of Semarang. Semarang City is one of the metropolitan cities in Indonesia with a high population density and, thus, a significant level of urban development. Continued development reduces green open spaces, even though these are crucial to improve the urban microclimate. This research aims at providing information to maintain the availability of green open spaces in Semarang City.

Thermal comfort level expresses the influence of microclimate on the human condition. The variables used in this study include the area of green open spaces, air temperature, humidity, vegetation density and the level of thermal comfort. This research will produce a map of the distribution of green open spaces, a map of vegetation density, a map of the distribution of the level of thermal comfort and how much green open spaces influence the surrounding temperature conditions. Besides that, from this research, it will be known which areas have a level of comfort that is classified as uncomfortable, a result that can be used as input to improve the local microclimate to provide comfort for the community in carrying out daily activities. It is hoped that this research can benefit the broader community regarding the importance of maintaining green open spaces in urban areas so that environmental conditions are maintained for comfortable living.

The workshop within the CCN 1 of the Agricultural Virtual Laboratory (AVL) aims at providing a good understanding and first hands on training to the paying agency.

The Sen4CAP project developed, validated and demonstrated an open-source toolbox (Sen4CAP system), which can process automatically Sentinel-1 SLC and Sentinel-2 L1C or L2A time series into a set of products which are relevant for the new Common Agricultural Policy. The main users of this toolbox are national Paying Agencies (and/or their sub-contractors specialized in EO), but also the private sector and researchers.

The Sen4CAP project fully relies on CREODIAS for the EO processing, and was already supported by NoR.

The Panta Rhei conference has the goal to facilitate the transfert knowledge between the Paying Agency. This opportunity is unique to express the importance of the Sen4CAP system to it’s main users. The workshop is a side event of the conference and has already 58 participants register. The workshop

will be focusing on two mains aspects : 1. communication of the main evolutions of the system up to now (and it’s futur evolution in 2022 thanks to the CCN 1 of the AVL). 2. Performing an hands-on-training with the system for the new comers (from the download of the images from the right dataset up to the generation of more advance products) and a question and answers session for the more advances users.

A previous request of sponsorship (22010a) was introduced to ingest the new dataset generated in the 2022 R&D activities.

Accurate information pertaining to the spatial distribution of various tree species in a forest stand is crucial for better monitoring and management of boreal forests. Such wall-to-wall information is lacking from field-based forest inventories. Meanwhile, remote sensing techniques based on satellite and

Unmanned Aerial Vehicle (UAV) data promises to highly reduce this information gap. The end result would be a publication and/or technical note that describes how ESA’s satellite date can be used along with UAV image data for better forest tree species mapping in boreal conditions. The benefit of userready data available from processing platforms (like F-TEP) will also be highlighted in the document.

The beneficiaries of this research project will be forest stakeholders such as forestry companies and government agencies.

The EU Copernicus programme has established itself globally as the predominant spatial data provider, through the provision of massive streams of high resolution earth observation (EO) data. These data are used in environmental monitoring and climate change applications supporting European policy initiatives, such as the Green Deal and others. To date, there is no single European processing back-end that serves all datasets of interest, and Europe is falling behind international developments in big data analytics and computing. This situation limits the integration of these data in science and monitoring applications, particularly when expanding the applications to regional, continental, and global scales.

The proposed C-SCALE (Copernicus – eoSC AnaLytics Engine) project aims to federate European EO infrastructure services, such as the Copernicus DIAS and others. The federation shall capitalise on the European Open Science Cloud’s (EOSC) capacity and capabilities to support Copernicus research and operations with large and easily accessible European computing environments.

“BACKGROUND: Canopy height is a fundamental geometric tree parameter in supporting sustainable forest management. Apart from the standard height measurement method using LiDAR instruments, other airborne measurement techniques, such as very high-resolution passive airborne imaging, have also shown to provide accurate estimations. However, both methods suffer from high cost and cannot be regularly repeated.

GOAL: In our study, we attempt to substitute airborne measurements with widely available satellite imagery. In addition to spatial and spectral correlations of a single-shot image, we seek to exploit temporal correlations of sequential lower resolution imagery. For this we use a convolutional variant of a recurrent neural network based model for estimating canopy height, based on a temporal sequence of Sentinel-2 images. Our model’s performance using sequential space borne imagery is shown in preliminary results to outperform the compared state-of-the-art methods based on costly airborne single-shot images as well as satellite images.

PREVIOUS WORK [1]: In our previous study, we adopted a neural network architecture to estimate pixel-wise canopy height from cost-effective spaceborne imagery. A deep convolutional encoderdecoder network, based on the SegNet architecture together with skip connections, was trained to embed the multi-spectral pixels of a Sentinel-2 input image to height values via end-to-end learned texture features. Experimental results in a study area of 942 km2 yielded similar or better estimation accuracy resolution in comparison with a method based on costly airborne images as well as with another state-of-the-art deep learning approach based on spaceborne images.”

The overall project aim is to access karst caves in remote places in Greenland and sample the speleothems (mineral precipitates on cave walls). From these can be extracted geochemical signals that relates to climatic variations at that location. The image analysis is to be used for planning a field expedition in the summer 2023. The main purpose of the field expedition is to collect speleothems from caves in East Greenland to provide unique data about climate variations in Greenland prior to the time interval covered by data from the Greenland ice-cores (~ 130.000 years). Such data are valuable for calibrating and improving climate models, especially for the Arctic region and the Greenland Ice Sheet (GIS). Traditionally the climate models rely on calibration data from marine sediment cores and ice cores. Recent developments in a suite of techniques (stable isotopes, radiometric dating, etc.) have provided scientists unprecedented opportunities to advance the understanding of mineral deposits in caves (so-called speleothems), although nearly exclusively such studies focused on temperate and subtropical regions (Fairchild & Baker 2012; Wong & Breecker 2015; Comas-Bru et al. 2020). Data from the Arctic are rare and until now only one single example from northern Greenland has been published (Moseley et al. 2021), covering a time-window of ~50,000 years dated at around 550,000 years ago. Additional data from Greenland cave speleothems may provide a better understanding of climate changes through the very important period from 2.5 million to 130,000 years ago including Pleistocene glaciations and interglacials, and will thereby serve to narrow uncertainty in the future predictions of the Arctic climate and the fate of the GIS under the progressively warming world. In order to plan and optimise fieldwork, satellite images will be used for the initial mapping of potential locations for cave entrances. The geochemical analysis data will be made publicly available and the results will be published.

CMIX II is the second edition of the joined ESA and NASA Cloud Mask Intercomparison eXercise activity in the frame of CEOS WGCV. It is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions.

Within the second addition of CMIX, dedicated reference datasets will be cerated to validate the participating cloud masking algorithms. One of these datasets is an expert pixel collection conducted on Sentinel-2 L1C and Landsat 8/9 Level1 data. Together with the participants, it was decided to provide information on cloud optical depth (COD) in addition to the expert classification, to have a numerical reference on different cloud transparency classes. To derive COD for any Sentinel-2 L1C or Landsat 8/9 L1 product, a surface reference is required. This reference can be a surface albedo or anything comparable.

In context of CMIX II an approach was developed using all S2 L2A data within an 18 day window of all years since the S2 launch, do derive a longterm average. The same is done for L8 L2 data. For this approach only the read and NIR band of the two sensors is needed, as well as cloud masking bands. The resulting product will called Land Surface Reflectance (LSR). The LSR can be used as a reference to estimate the COD. The reference dataset including the COD estimates will be published at the end of CMIX II. The reference dataset will comprise approx. 100 Sentinel-2 and 100 Landsat 8 products, the expert pixel collection and the COD estimates for all collected pixels.

The objectives of the project include the feasibility study on the use of VHR optical data for coastal erosion studies and the production of coastal erosion rates from VHR optical data for selected areas along the north Dublin coastline. The results will be shared over the GSI web mapping services for free as an example of the use of VHR to monitor coastal erosion.

The goal of the project is the measure coastal erosion/shoreline change rates along the County Wicklow coastline in Ireland. The results of the project will allow us to give an up to date, accurate, and relevant synopsis of how the soft sediment coastline of Wicklow in the east of Ireland has changed over the last

two decades and what its current state of erosion/accretion is. Using orthophoto data collected in Ireland since 2000 it has been possible to gain an understanding on shoreline change over the timeframe 2000-2012, however the quality of data has improved significantly in recent times, which is a strong opportunity to understand more recent changes to Irelands coastline.

As a results, we are trying to access VHR commercial satellite data to digitize shorelines ( e.g vegetation line) between 2012-2022 and compare the results with the orthophotos. The coastal area in question is composed of three main environments exposed bedrock, lowland beach or marshland areas, and soft sediment cliffs, with some areas classified as Special Areas of Conservation by Irelands National Parks and Wildlife Service. As climate change and rising sea levels begins to take effect in the coming decades it is important to have a good baseline understanding of the fluctuation of shorelines, especially those in low lying areas that are vulnerable to coastal erosion or habitat loss. This stretch of coastline contains several significant urban areas which represents a significant anthropogenic influence on the project, as shoreline change/coastal erosion rates can help influence informed decision making along the Irish coastline with respect to This project can feed into ongoing coastal vulnerability and coastal erosion projects occuring in Ireland and throughout Europe.

In this project, it is proposed to create a high resolution (<10m) coastal typology of the European coastline, which distinguishes land use / cover classes relevant to coastal flooding and erosion. During this sponsorship we will develop a methodology to classify the satellite imagery. Upon success we will scale this to the whole European coastline.

Previous research in a GIS Programming course requested teams of 2-3 students to develop a state-of-the-practice QGIS plugin (Anbaro#lu 2021). Consequently, students relied on Git to collaborate with each other while developing their plugins, did unit testing, provided language support and documented their plugins using Sphinx. Although, students learned valuable technical and practical skills, in order to have a critical spatial data science perspective, more theory should be integrated into teaching (Holler 2019, Kedron et al 2020). Therefore, the objectives of this experiment is to investigate how students utilise an open-source Python package, x2Polygons, to find the distance between georeferenced polygons. For this each student will digitise a number of polygons, with varying complexity – in terms of the number of edges each building possess and evaluate how different distance measures such as the Hausdorff distance; Chamfer distance, PoLiS distance (Avbelj et al 2021) and turn function distance correlate with each other. In this way, they will be able to assess the advantages and limitations of different distance measures.

The main objective of this project is to study the complementarity of spatial optical imaging, structural information from Synthetic Apertuge Radar (SAR) and environmental characterization data to model maize and sunflower seed production by aggregating these observations of different spatial and temporal resolutions. The thesis work will be based on Syngenta’s plot network in several parts of Europe and North America, where some varieties of maize and sunflower are evaluated under different environmental conditions.

The Feature Engineering of satellite observations used in the development of machine learning models will seek to estimate varietal parameters-functional extracts-which define, on the one hand, the phenology, but also the different varieties’ response to abiotic stress, and mainly to water stress. In the case of phenology, we will study the parameters that determine the development response to temperature and photoperiod, and that predict specific crop stages. The functional traits that characterize the response to abiotic stresses will make it possible to identify, from multi-environment observations, the most efficient varieties, and to predict their behavior and yield.

Many strong active faults have developed within the Tibet Plateau, Tienshan and its adjacent regions, forming multiple seismic zones due to the collision and continuous extrusion wedging between the Indian and Eurasia plates. Those faults directly control the spatial distribution of severe disaster zones in mega-seismic areas. But the lack of quantified descriptions of geology and geodesy in this area makes it very limited to understand its geophysical environment and rupture process of active faults. This study focuses on strong active fault zones in Western China, especially in the central Qinghai-Tibet Plateau and Tienshan region. GPS and InSAR will be used to monitor the crustal deformation and to derive an accurate 3D velocity map of the area. We expect to densify the existing GPS network, form several profiles across those active faults, and then integrate the GPS and InSAR measurements to derive the velocity maps and geometry of different segments of the faults, cooperating with geology and geophysics data. It can elaborate the advantages of two means and get the fine fault monitoring and structure analysis to reveal the graben deformation characteristics, tectonics, deformation pattern and evolution mechanism of the studied regions. Finally, we will inverse the lock depth of different segments and investigate the relationship between crustal deformation features and strong earthquakes and the relationship between the lock depth and deep structure. It will improve the ability of earthquake forecast by revealing the kinematics pattern and dynamics and dynamics background of the continent deformation in China and its adjacent regions.

Danube Data Cube (DDC) is a regional data exploitation platform built on and follows the logic of the Euro Data Cube (EDC) infrastructure, a computational environment reflecting the Digital Twin Earth concept of the European Space Agency to support sustainable development. DDC is a cloud-based platform with data and analysis tools focusing on the Danube Basin. As a regional platform service, it demonstrates the data cube technology’s data storage and analysis capabilities, maximizing the benefit of the synergy of satellite and ancillary data with dedicated analysis tools. The DDC concept includes extensive Machine Learning capabilities, including analytical tasks and decision support algorithms. One of the key themes of the platform is water management, from regional strategy and public information to field-level irrigation management.

Currently, DDC works on a regional and a local (field-level) showcase. Both are related to water management.

Among the renewable energy sources, solar and wind are rapidly becoming popular for being inexhaustible, clean, and dependable. Meanwhile, power conversion efficiency for renewable energy has improved with great technological leaps. Following these trends, solar and wind will become more affordable in years to come and considerable investments are to be expected. As solar and wind plants are characterized by their high site flexibility, the site selection procedure is a crucial factor for their efficiency and financial viability. Many aspects affect site selection, amongst them: legal, environmental, technical, and financial. Today, information gathering for site selection assessments is a manual and time-consuming process. The main objective of this project is to develop a dataset of existing solar power plants* by applying computer vision on satellite imagery.

Objective 1 (O1): Achieve 90 % accuracy for a specific data layer* using SOTA deep learning models.

Research Question 1: Can the required accuracy be achieved with publicly available 10×10 meter

image resolution, or must higher resolution imagery be used?

Research Question 2: How can the training-data creation process be made for the specific data layer to

achieve the required accuracy?

Research Question 3: How can the training of the model be made for the specific data layer to achieve

the required accuracy?

Since the project is researching the process of site selection and the utilization of data for renewable

energy projects, the idea contributes to positively influence the UN’s Sustainable Development Goal 7;

Clean energy for everyone. For the region to be able to produce enough clean energy, it is necessary to

accelerate the development of renewable energy projects.

The objectives of the project are:

1. Assessment of decadal Mass Balance and Ice Thickness of glaciers of Sikkim Himalaya (Study

Area 1200 Sq. Km)

2. Estimation of Glacier Facies using GLCM and Random Forest Classification.

3. Movement analysis of glacier in Sikkim using PSI.

Statement on availability of results.

The results shall be available online through any suitable data sharing portal on request for research purpose only. It shall also be shared with nodal agency for sub-regional level policy response. Subregional policy requires concrete evidence especially in developing Nations like India.

The objective of this project is two-fold and the requested data can be used for both tasks while testing processing capabilities on The Food Security Platform (TEP). Firstly, we will demonstrate that it’s possible to do bottom-of-atmosphere (BoA) correction on Sentinel-2 Reflectance at Bottom of Atmosphere/VISTA Algorithm (available on TEP as ‘Vista_S2-L2A) using deep neural networks. We estimate that this can improve processing speeds by x100 to x500 while keeping accuracy high. This is inspired by an existing algorithm, developed for another project, that in production as a side effect efficiently fixed incorrect Sen2Cor bottom of atmosphere correction. This is in particular interesting on, important and frequently used algorithms with long processing times like BoA processing algorithms. The results will be avg. pixel error measured against ground truth imagery. We will also present the relevant processing speeds improvements and requirements to run said algorithm (eg. GPU accelerated processing). The results will be made available on TEP as ClearSky Vision demo data, and if possible produced on TEP. It will, furthermore, be measured against data in-sample and out-of-sample, and the project will be finished off by producing a tile unavailable on the platform. This project has the potential capability of greatly reducing required resources for BoA correction on Sentinel-2 imagery by doing it in

a fraction of the time (leaving data storage as the final limitation). Not only making it a fast and efficient process but it also makes near-real time monitoring more achievable.

ClearSky Vision has already developed an algorithm for cloudless Sen2Cor imagery (using deep learning and multiple satellites for data fusion). This approach won ClearSky Vision the Copernicus Masters Bay Wa competition in 2020. It combines Sentinel-1, Sentinel-2, Sentinel-3, and Landsat 8 into daily cloudless Sentinel-2 imagery. This project will further prove, to what degree cloudless results on Vista S2-L2A will match the accuracy from prior cloudless Sen2Cor imagery tests. The objective is to determine whether this (more complex) processing method will make the cloudless process more difficult or what’s more likely, improve the consistency of the output. The results will be made available on The Food Security Platform as ClearSky Vision demo data (10 spectral bands).

DeepESDL users or teams will be provided with individual subscriptions for external services to ensure that dedicated resources are available to them. The requested subscription is required for the first set of Early Adopters, which are currently onboarded, and their associated use cases as well as for the DeepESDL consortium to integrate the Sentinel Hub service, demonstrate and validate ts functionality and for using it in training sessions for new users.

The project will use differential interferometric Synthetic Aperture Radar techniques (DInSAR) to measure land deformation caused by earthquakes and land subsidence. Geological instabilities could cause the differential movement of ground at different depths. This phenomenon is a gradual settlement of soil that causes inundation of land, expansion of flooding areas, disturbance of drainage systems, changes in slopes, and damages to infrastructure foundations in urban areas. This study will also analyze the deformation’s behavior and cause of subsidence within the research area. Moreover, the study will try to understand the necessary connections and interactions between people and natural events to prevent or lessen the extensive social, economic, environmental, and infrastructure effects. Supporting information requires accurate and timely change detection on Earth’s surface to make better decisions about land deformation and the event’s temporal ramifications. To enhance the measurement of small-scale surface deformation using SAR Interferogram. The information, including detailed coseismic deformation based on the study’s interferometric results, will be helpful in the community’s disaster management and mitigation activities. The results will include the land deformation map and the Subsidence map of the study area. The aim is to provide information to urban planning and management authorities. Finally, the project will be used to gain expertise in using the Geohazard platform.

The access to Earth Console is made in the frame of Proposal DETECT-B01, which is part of the Collaborative Research (CRC) 1502 of DFG (https://www.lf.uni-bonn.de/en/research/crc-detect). The main goal of DEECT-B01 is to estimate river discharge and water storage change from space using satellite altimetry. The central hypothesis of DETECT-B01 is that the new generation of space-borne altimeters, including Delay Doppler(DD), laser and bistatic SAR altimeter techniques, outperform conventional altimetry(CA) and in-situ measurements. They provide surface water levels and discharge of higher accuracy and spatial and temporal resolution thanks to the new river slope and width parameters. The better sampling will improve flood event detection and long-term evolution estimation, providing valuable further information to the overall CRC. In the first four years of its 12 years possible duration, two research questions have been addressed in the CRC:

1. How can we fully exploit the new missions to derive water level, discharge, and hydrodynamic river processes?

2. Can we separate natural variability from human water use?

Over the last decade, with SAR altimetry data, merging innovative space observations with in-situ data provides a denser and more accurate two-dimensional observational field in space and time compared to the previous two decades. This process allows better monitoring of water use’s impact and characterising climate change. River discharge and water storage change innovatively derived from the nadir and swath-altimetric measurements of river slope, height, and width will enable to validate the modelling (e.g. through budget studies) and will be used for assimilation in the IMS. A multi-sensor database will be built starting from 1993 and used in the Integrated Monitoring System (IMS) from other partners in the CRC. B01 will also monitor the exchange of water between rivers, lakes and reservoirs and the impact of natural and human disturbances, like water use. The project will contribute to the CRC’s key objectives in that it addresses the surface water compartments by improving new observation types and including them in the modelling.

Object detection is one of the mandatory steps in transferring imagery data into land cover information. Deep machine learning networks have shown automatic object detection capabilities and generated promising results. The patch-based Deep Neural Network (DNN) is one of the architectures designed for pixel-based object detection in aerial images.

Road extraction from remote sensing images is significant to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains challenging due to complex circumstances and factors such as occlusion.

Road extraction from remote sensing images is significant for updating geographic information systems (GIS), urban planning, navigation, and disaster assessment. In the past, the most widely used way to extract roads was through manual vision interpretation, which takes a lot of time and has a high labour cost, and the extracted results may vary due to the differences of interpreters. Automatic road extraction technology can improve the efficiency of road extraction, so it has become a hot issue in this field. This deep learning model is used to extract roads from high-resolution satellite imagery.

Road layers are useful in preparing base maps and analysis for urban planning and development, change detection, infrastructure planning, and various other applications.

Digitizing roads from imagery is a time-consuming task and is commonly done by digitizing features manually. However, deep learning models are highly capable of learning these complex semantics and can produce superior results. Furthermore, deep learning models can automate this process and reduce the time and effort required for acquiring road layers. My project is to train a model that can detect and build road networks anywhere in Egypt.

GEO Mountains (https://www.geomountains.org/) is an initiative of the Group on Earth Observations (GEO). Mountainous regions provide numerous goods and services to both highland and lowland populations globally. However, climatic and environmental changes, large-scale political and socio-economic transformations, and the unsustainable management of natural resources threaten this increasingly. Decisions on policy and investment, from the level of local governments to international agencies, must be based on knowledge that reflects both the generalities and specificities of mountainous regions. The paucity of observations from highelevation regions and associated major gaps in the understanding of mountainous systems thus represent key challenges that must be overcome. In October, GEO mountains released amajor iteration (v2) of the Inventory of In Situ Observational Infrastructure. This update includes many more researchoriented mountain observatories, operational stations, and locations where longterm monitoring is being undertaken. Looking ahead, GEO Mountains will consider providing data storage and linking for those sites that are not able to make their data available in an open repository otherwise. Also capturing extensive metadata for each site to facilitate a comprehensive, interdisciplinary “gap analysis” of in situ mountain observations (i.e. for many variables and with respect to geography, time, and elevation). The project will use the GEP services to develop improved inventories of past avalanches and landslides in remote mountain regions of the world, including the Andes, HKH, Central Asia, and East Africa.

Objectives of this project are: • Develop and validate methods for determining the stage and advancement of desertification via diffuse reflectance spectroscopy in the MIR aiming at obtaining prediction models for chemical and physical attributes in soils under intense degradation process. • Build a spectral library using the wavelengths observed in soil samples from the region, highlighting the distinction between the spectra observed in desertification area soil samples; • Understand the link between spectral attributes and chemical and physical attributes of the studied soils; • Prepare maps of the spatial variability of soil attributes, using the results obtained from analyzes carried out in the laboratory (measured values) and obtained by sensors (predicted values) in the study area. • Create land use and land cover maps using high-definition satellite imagery data provided by Sentinelhub.

The vision of the DE Africa is to provide a routine, reliable and operational service, using Earth observation to deliver decision-ready products enabling policy makers, scientists, the private sector and civil society to address social, environmental and economic changes on the continent and develop an ecosystem for innovation across sectors. The mission of the DE Africa is to process openly accessible and freely available data to produce quality products. Working closely with the AfriGEO community, DE Africa will be responsive to the information needs, challenges and priorities of the African continent. DE Africa will leverage and build on existing capacity to enable the use of Earth observations to address key challenges across the continent.

DE Africa portal: https://www.digitalearthafrica.org/

Africa has many agriculture challenges so monitoring and management was identified as high priority. Addressing these challenges requires better technology and policies to improve the management of small farms. Airbus Pleiades and SPOT have an impressive acquisition capacity that offers better earth observations of the area of interest and it is useful for monitoring of environmental practices and agri-environmental measures, which is very useful for the primary focus of the project: developing prototype crop delineation method as part of a Food and Agriculture Organization of the United Nations (FAO). The method is developed for country government users. The exact test area will likely be in Rwanda. For the small farms, higher resolution is desired.

Airbus Pleiades and SPOT high-res images were recognized as very useful for delineation of smaller crop fields.

The aim of the pilot is to develop a pan-European scale drought impact monitoring platform using the new CLMS service High Resolution Vegetation Phenology and Productivity (HR-VPP) derived from Sentinel 2 images.

This request is related to the “Drought impact monitoring platform” activity, part of Euro Data Cube project (reference 2020-12-06-DCFS-Proposal), service consumption already confirmed by ESA. The request is done on behalf of EEA in scope of ETC/DI project

CentraleSupélec – a French high school of engineering – organizes a course on Satellite Earth Observation dedicated to around 110 first year students, from the 22nd November to the end of January. This course is an introduction to optical and SAR remote sensing. It is based on the use and processing of ESA’s Sentinel images. The support of EOCARE is requested to allow the students to carry out mini-projects on 3 topics at the end of the course.

Agricompas is developing a data analytics platform in an IPP (International Partnership Program) project funded by the UK Space Agency. EcoProMIS or Ecological Production Management Information System aims to provide all stakeholders involved in rice production with valuable insights in farmer and crop performance. Information is provided free to farmers (who are sharing in-situ data) to access information on crop management, soil and environmental conditions during the crop production cycle to improve decision making. Paid Analytics as a Service is provided to various stakeholders. A matchmaking platform will nurture stakeholder relations with safe and sound information of technical, economic, social and environmental processes. Farmers can place product and service requests with providers that can develop and tailor their offerings based on better farmer and field information.

EcoProMIS uses Earth Observation technology, especially from Sentinel satellites, to monitor maximal 500,000 hectares from up to 16,000 Colombian rice farmers in the four main rice production regions. Initial results of the project have included significant advances with the usage of Sentinel-2 imagery to derive various products, which monitor the situations on farms, and lead to direct knowledge which can be used to support decision making on the fields. The products derived from the Sentinel-2 imagery are also being used as inputs to crop models, allowing for predictions of important insights such as crop yields. We apply a two-way verification of crop performance. Bottom up with parameter data feeding into crop models and top down with satellite imaging. The two methods create a digital twin that with AI and machine learning leads to improved algorithms requiring less data while optimising crop monitoring and evaluation accuracy.

The project consists of disseminating Copernicus values with data from Sentinel-2, Sentinel-1 and Sentinel-3. Spain is one of the countries in the Mediterranean basin where more forest fires occur, being one of the great problems of our forests. Our Public Educational Center trains future professionals in the extinction of forest fires. The project consists fundamentally in carrying out analyzes of forest fires that occur during the year, especially those that have an area greater than 500 hectares. The work will be published on social networks, specifically on Twitter, in the profile of @eforestal, and published on the website that I share with the students of the educational center: http://almazcara.forestry.es

Here you can see a sample of our publications this year using Sentinel-2 images for analysis of wildfires: https://e.forestry.es/GIF2021

Objectives. The main aim of the research project is to evaluate effects of agricultural expansion and practices on water quality of the Upper Lunsemfwa Catchment. Specifically, for the NOR, the research seeks to determine the extent of agricultural expansion in the Upper Lunsemfwa Catchment from the year 2015 to 2020. The envisaged output of the project is to produce detailed mapping of the area of interest in terms of extent of cultivated area, specific crops and their intensity including:

• Production of the general land cover and land use maps of the;

• Production of the spatial extent of the agricultural domain i.e. annual binary crop/non-crop maps; and

• Production of maps of crop inventories of what, where and when crops are grown.

The format of the products envisaged include DIMAP format including GeoTIFF raster images, UTM – UPS/WGS 84 Projection, XML file meta data. Statistics of the to be classified crop areas will further be generated to quantify area coverage for each identified crop. The results of the project will be available to beneficiaries via peer reviewed open access scientific publications.

This request for NoR is an extension to the initial sponsorship that was given for Project ID 60792 for the period October 2020-February 2021. The extension is being requested to enable usage of the Sen-4CAP mapper that has the potential to map grassland and permanent crops which are a significant feature of the

area of interest. In addition, the ability to generate shapefiles documenting crop types observed and their associated attributes would add value to the product considering the end result desired i.e., relate crop type and fertilizer use to water of adjacent rivers and streams in the area of interest. Monitoring of agricultural practices i.e., crop planting and harvesting/clearing would aid in determination of cropping patters. The extension is further being requested to allow for the inclusion of additional in-situ data that was collected in the period March to May 2021. The additional crop field data was necessitated by the need to capture the fragmentation and heterogeneity of crop fields managed by peasant, small scale, and emergent farmers. Farming patterns of peasant, small scale and emergent farmers are fragmented and heterogeneous owing to management practices of the said farmers. Nonetheless, it is these farmers that provide for approximately 80% of the Zambian national food basket and hence contribute largely to the food security of the nation. Understanding the peasant, small scale and emergent farmers cropland extents, crop types, cropping patterns and cycles will lead to a better understanding of the arable land use and thus help ensure sustainability of agriculture while ensuring food security. This is envisaged in the production of timely, spatially explicit, and precise information that will aid decision making

Feral cats have caused the decline and extinction of many native species in Australia. There is also evidence that their impacts can be greater after fires when vegetation is removed, making it easier for them to hunt. In this project, we are using field experiments to assess the relative impacts of fire and predators on native prey populations, including identifying the post-fire period when prey are most vulnerable and determining how different management strategies could benefit native fauna (i.e. burning and cat baiting). The specific aims of the project are:

– Quantify changes in habitat structure in response to patch burning.

– Examine how patch burning by Indigenous Rangers of the Western Desert affects mammal and reptile communities in areas with and without predator control.

– Examine how predators (cats, dingoes) respond to patch burning.

– Determine if the severity and patchiness of burning affects wildlife populations, and whether these varies according to predator control.

The ALBIOM project (ALtimetry for BIOMass) proposes to derive forest biomass using SAR Altimetry Data from the Copernicus Sentinel-3 (S3) Mission. The Project is coordinated by Leila Guerriero.

Who will benefit from the project results: the scientific community and humankind. Results will be made available via: publications, abstracts, conferences

ENVISION aims to fulfil the need for continuous and systematic monitoring of agricultural land, shifting the focus from fragmented monitoring limited to specific fields and dates to territory-wide and all-year-round monitoring. It will make use of heterogeneous types of available data (EO-based, in situ, open data, and historical on-field check data) and state-of-the-art technologies and methodologies (automatic pixel/texture/object-oriented change detection and classification methods, machine learning, data fusion, multi-source and multi-temporal data management) for providing a fully-automated and scalable toolbox of services, built in close interaction with its future customers. ENVISION will fully exploit the wealth of data made available through GEOSS and Copernicus and its synergetic use with other data to develop data products such as Cultivated crop type maps; Soil Organic Carbon; Vegetation status; Crop growth (distinction of organic – conventional farming); Grassland mowing /ploughing; Soil erosion. The ENVISION toolbox will comprise a monitoring service of sustainable agricultural practices, tools that Pas & CBs can provide to farmers for adhering to environmentally friendly agricultural practices, and an Add-on Development Tool. The project will be tested and validated in a pre-operational environment by potential future customers of its products and services. ENVISION will have three categories of business cases (Monitoring of: multiple environmental and climate requirements of CAP, soil condition, and organic farming requirements) and will also be tested by a group of Lighthouse Customers. A market analysis, business model experimentation techniques and appropriate decision-making tools will determine the commercially viable business models for the services and products of ENVISION, define alternative business models, understand their implications and identify those that will create the most significant value. Role in the project ITC’s main tasks in the ENVISION project, together with the DIH AGRIFOOD, are linked to Dissemination & Exploitation management, where ITC will plan, draft and develop the Dissemination and Communication Plan as well as manage, coordinate and implement dissemination and communication activities according to this plan. Furthermore, ITC will increase the capacity of companies and organisations that offer commercial products to develop new and improved products and services by building up on the ENVISION solution. ITC will also be actively involved in commercialising and exploiting the ENVISION services and products and in identifying user requirements and needs.

EO Africa R&D Facility is the the flagship of the EO AFRICA initiative of ESA. The overarching goal of the Facility is to foster an African-European R&D collaboration enabling an active research community and creative innovation processes for continuous development of EO capabilities in Africa. The R&D Facility will review the African EO research challenges and issue research calls for addressing the most relevant ones. It will offer modern cloud computing & digital tools for the researchers and support a range of collaborative activities and initiatives between the African and European research communities.

Telespazio UK Ltd are the prime contractor for ESA’s “EO Exploitation Platforms Common Architecture (EOEPCA)” initiative. EOEPCA aims to facilitate adoption of a freely available common architecture that supports a paradigm shift from “bring the data to the user” (i.e. user downloads data locally) to “bring the user to the data” (i.e. move user exploitation to hosted environments with collocated computing and storage). This leads to a platform-based ecosystem that provides infrastructure, data, computing and software as a service. The resulting Exploitation Platform is where scientific and value-adding activities are conducted, to generate targeted outputs for end-users.

The focus of the Common Architecture is to define an open architecture using open interfaces that facilitate the federation of services in the “EO Network of Resources”, and to develop a Reference Implementation of the architecture for deployment as an operational service. The goal of the EOEPCA project is specifically tied to the goals of the Network of Resources (NoR). EOEPCA will define an architecture that facilitates the success of the NoR, comprising building blocks with well-defined interfaces based upon open standards. The architecture and its interfaces are designed to be reusable by third-parties provisioning platforms with the NoR. Use of the Common Architecture will encourage platform providers to have consistent interfaces with other platforms in the NoR ecosystem, and so facilitate interoperability that allows platforms to share their resources.

We just recently adapted this in-class project and founded a new student association, and we would like to resume our activities to promote space and earth observation, promote data extraction and popular science, and to push the limits of our different sub-projects, which we feel have barely scraped the surface of their potential.

The Open Science Catalogue is one of the elements contributing to an Open Science framework and

infrastructure, with the scope to enhance the discoverability and use of products, data and knowledge

resulting from Scientific Earth Observation exploitation studies. This SAP activity aims to: enhance the Open Science Data Catalogue’s capabilities for data governance expand the catalogue with a data storage and long-term preservation component enhance interoperability and interconnectivity with the other open science and collaborative development systems enhance data indexing and discoverability in the cloud and enhance programmatic access to the catalogue.

The Open Science development team will work in an Agile way with ESA as the primary Product Owner supported by TPZ-UK. The bulk of the implementation is directly relevant to the Resource Management and is allocated to EoX and EOfarm.

The consortium of the EOStat project for Statistics Poland proposes to implement the EOStat system of crop recognition and yield prediction in the territory of Ukraine. This will make it possible to estimate the yield losses for 2022 compared to previous years 2017 – 2021.

Objectives: The ESA “Sentinels for Agricultural Statistics” (Sen4Stat) project aims at facilitating the uptake of sentinel EO-derived information in the official processes of National Statistical Offices (NSOs), supporting the agricultural statistics. The project is working in four pilot countries: Spain, Ecuador, Senegal and Tanzania, thus addressing a wide diversity of both cropping systems and agricultural data collection protocols.

In close interaction with its pilot countries, the project conducted an in-depth review of how efficiently integrating EO data in their current NSOs workflow. National use case studies were defined: i. Coupling crop type maps with statistical ground surveys to derive crop area estimates; ii. Coupling crop type maps, biophysical variables, crop yield in situ data and district-level official crop yield statistics to derive crop yield and production estimates; iii. Using crop type maps, biophysical variables and statistical ground surveys to disaggregate the agricultural statistics to small administrative units and improve the statistics timeliness through the provision of early crop area and yield indicators; iv. Relying on cropland and crop type maps to build or update sampling master frames and optimize the sampling design; v. Supporting the official reporting of the SDG indicators 2.4.1. and 6.4.1. The project will develop validated algorithms and open source tools supporting these use cases and demonstrate them through the production of national products and best practices. It will also conduct training and capacity building activities on these tools and algorithms, thus supporting the uptake of EO technology by the NSOS.

This project aims at studying anthropogenic CO2 emissions as well as biological CO2 cycles from satellite observations using machine learning tools. The objectives are to develop algorithms to 1- Learn to detect plumes of CO2 emissions; 2- Estimate CO2 local emissions from plumes; 3- Predict monthly CO2 fluxes at a global scale. An exploratory subscription to Sentinel Hub will be very helpful, as easy to use data is basic for the project. The results of this project will be published in peer reviewed journals and presented at international conferences, the algorithms will be well documented and made public in GitHub.

The core objective of the EU Horizon project FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner. To reach this objective, we propose creating the FAIRiCUBE HUB, a crosscutting platform and framework for data ingestion, provision, analysis, processing, and dissemination, to unleash the potential of environmental, biodiversity and climate data through dedicated European data spaces. Within this project, TRL 7 will be attained, together with the necessary governance aspects to assure continued maintenance of the FAIRiCUBE HUB beyond the project lifespan. This project’s goal is to leverage the power of Machine Learning (ML) operating on multi-thematic datacubes for a broader range of governance and research institutions from diverse fields, who at present cannot easily access and utilize these potent resources. Selected use cases will illustrate how data-driven projects can benefit from cube formats, infrastructure, and computational benefits. They will guide us in creating a user-friendly FAIRiCUBE HUB, which is tightly integrated to the common European data spaces, providing relevant stakeholders an overview of both data and processing modules readily available to be applied to these data sources. Tools enabling users not intimately familiar with the worlds of EO and ML to scope the requirements and costs of their desired analyses will be implemented, easing uptake of these resources by a broader community. The FAIR sharing of results with the community will be fostered by providing easy to use tools and workflows directly in the FAIRiCUBE HUB.

In partnership with the national statistical offices and line ministries responsible for SDG implementation, the project EO Solutions for national SDG monitoring will develop innovative EO data processing and data analytics workflows exploiting EO platforms, integrating them within national systems and processes on SDGs, to showcase the adequacy of EO solutions for SDG monitoring.

Lakes are one of the primary sources of freshwater, and their size variations provide information critical to their sustainable management in the backdrop of seasonal and climatic changes. Due to topographic limitation and financial constraint, it will be difficult for any country to install gauges in the Lake ecosystem. Therefore, the Altimetry satellite can assist the government to

estimate water level which will be known as Virtual station. Satellite altimetry is an innovative method, currently being used to monitor water levels over oceans and inland water bodies. In this study, multi mission altimetry satellites are being used to study the water levels of Inland water. The Manchar Lake is significant for its ecological, social, and economic value but is not monitored regularly and hence cannot be managed well. Due to the unavailability of gauge data, it is challenging to calculate Lake’s water balance directly. Sentinel3A , the SRAL (SAR Altimeter) instrument mission with short historical data, provides the water level heights for our study area from July 2016 till April 2019.

In this study, Sentinel-3 altimetry data will be used for monitoring seasonal water levels of the Manchar Lake in Pakistan. After processing of Sentinel-3 data from Earth Console, further data will be processed in ArcGIS to estimate water levels of Manchar Lake. Sentinel derived water levels will be compared with Insitu data which assist in validation of sentinel 3 data. Who will benefit from the project results: irrigation department ,public health department, community water user, national disaster authority, water managers ,researchers , environmental protection agencies etc

Through the application of interferometry, the characterization of the phase and coherence variations, later the application of the STAMPS algorithm I intend to evaluate the surface variations in 3 high Andean salt flats of Chile, the first salt is the Coposa salt lake which maintains a close relationship with the surface hydrology and this can be distinguished in interferograms, so I seek to establish a mathematical relationship between interferometry and the intensity of precipitation events.

The second is the Salar de Atacama where anthropic activity related to lithium mining is considerable, so there is a relationship between deformation and surface and the extraction of lithium and underground water. Finally, I study the Salar de Llamara since this is a hydrogeological system very different from the previous ones, so it is possible to restrict and contrast the conclusions previously obtained.

Who will benefit from the project results: This is a research project leading to obtaining my degree in geology in Chile

-Results format: Through a PDF document validated by the university and free of charge through the university’s platform

Through the application of interferometry, the characterization of the phase and coherence variations, subsequently the application of the STAMPS alorithm, I intend to evaluate the surface variations in 3 high Andean salt flats of Chile, the first is the coposa salt flat that maintains a close relationship with surface hydrology and this can be distinguished in interferograms, so I seek to establish a mathematical relationship between interferometry and the intensity of precipitation events.

The second is the salar de atacama where anthropic activity related to lithium mining is considerable, so there is a relationship between surface deformation, lithium extraction and groundwater. Finally, I study the Llamara salt flat as this is a very different hydrogeological system from the previous ones, so it is possible to restrict and contrast the conclusions obtained previously. On the other hand, I will characterize the backscattering coefficient of the three salt flats to see how this characteristic can influence the results obtained, contrasting with LANDSAT 8 images. With this it is possible to obtain time series of deformation, phase, coherence and backscattering coefficient, which compared with the hydrological characteristics of the system speak of the dependence of these elements with the precipitation events, the change in the direction of the winds, the drainage network and in particular anthropic activity both associated with the extraction of minerals and the pumping of underground water. This research contributes to the understanding of the dynamism of the Chilean high Andean salt flats since these, due to their geological and hydrogeological characteristics, are an important source of water resources and understanding how their surface changes provides information on how the hydrogeological system changes and it is possible to interpret how the Climate change and variations in the intensity, duration and seasonality of rainfall affect them. On the other hand, due to the geographic location of the high Andean salt flats, their access is complex and dependent on many climatic conditions, so being able to obtain information remotely is essential.

The project will address a set of various use cases for the evaluation of various geological risks:

1. Ground deformation in the Canary islands;

2. Automatic identification and classification of deformation signals;

3. Ground deformation associated to Green Hydrogen injection;

4. Ground deformation for cross-border risk assessment at European level;

5. Ground deformation caused by groundwater extraction;

6. Volcanic deformation in El Salvador;

7. Geological risks in urban areas;

8. Ground deformation induced by underground mining in active mining

areas.

Farm0C is a Science Foundation Ireland funded project to demonstrate that intensive Irish dairy farming can become climate neutral and biodiversity friendly. Life cycle assessment, remote sensed habitat mapping and digitalization will be combined to create an app that assesses a farm’s emissions and biodiversity. The app will further suggest best practice and technology needed to reduce farm emissions to climate neutrality while also providing bespoke recommendations as to how to increase farm biodiversity. Using high resolution satellite imagery (0.5 m- 10m resolution) it is possible to pick out linear habitat features on farms. Linear habitat features comprise a majority of on farm habitats in Ireland and so are essential to map in any farm-scale habitat mapping exercise. EU legislation (see EU Biodiversity strategy 2030) is moving towards requiring 10% of farm area to be managed as ‘high diversity habitat features’. Accurately quantifying farm habitats requires the development of high resolution habitat mapping that can reliably map linear features. The first objective of work package 2 of the Farm0C project is to develop and train machine learning algorithms to map farmland habitat at unprecedented resolution (0.5m – 10m) to allow for accurate habitat extent estimates. Remote sensed habitat maps will be tested and validated by comparison to on the ground farm habitat surveys. The deliverable of this objective is a scalable, reliable, farmland habitat mapping algorithm using high resolution remote sensed data to allow farm scale habitat mapping in Ireland. Once tested and validated in Ireland, it will be extended to other dairying and livestock systems in Europe. The second objective of work package 2 is to build farm scale natural capital accounts based on the SEEA ecosystem accounting framework. The habitat maps derived from remote sensed imagery will be used as habitat extent accounts. Condition assessments will be attempted based on remote sensed imagery using variables such as age, width and if possible, height.

Remote sensed condition assessments will be validated on the ground to assess the feasibility of remote sensed ecosystem condition estimates. Finally, ecosystem extent and condition accounts will be transformed into ecosystem service accounts. The main ecosystem services which will be investigated are carbon sequestration and storage, grass productivity, water pollution remediation and biodiversity conservation. Remote sensed imagery will be used to explore to what extent these ecosystem services can be measured remotely, the goal being to develop models that can reliable measure ecosystem services that can be scaled to many farms. The final deliverable of Farm0C project is the app. The farmer is the end user, such that the farmer can request that their farm be mapped, resulting in habitat extent and condition accounts of the farm being produced from satellite imagery using the models developed in the project. We also envision that our models and IT will be of interest to regulators, there will be a need to monitor compliance once area-based farmland conservation targets become part of the Common Agricultural Policy.

Our EU Interreg project operates within the region of Botnia-Atlantica in central Sweden and Finland. It supports regional businesses to develop opportunities within the “new space” economy and involves ecosystem-building support to commercialize existing space-based data. A component of our project is an end-to-end satellite mission technology demonstration process named “KvarkenSat”, having hyperspectral imaging, positioning, AIS and radio communication capabilities.

To bring the region’s forestry sector and new technological developments together, the “Kvarken Space Economy” project is organizing a “hackathon” in March 2022 called “KvarkenSat Innovation Challenge 2022 on Sustainable Forestry”. (https://ultrahack.org/kvarkensat-innovation-challenge-2022). This challenge will network the regions forestry sector actors with the latest ideas from research institutions in Finland and Sweden ( Luonnonvarakeskus (Luke) and Svenska Landsbruk Universitet (SLU), respectively). The individual, student and company start-up participants in the challenge will work with remote sensing observations and spatially dependent modeled results to address four research fronts important to the participating forest sector actors. The Forest-TEP platform will be demonstrated and offered as a platform for developing solutions during the challenge.

The four themes include supporting more timely estimations of soil moisture, preventing damages caused by spruce bark beetles, learning how to reduce damage to the forest ground caused by forest machines and developing new digital concepts for the forestry value chain.

The participating forest sector companies will have the possibility to work with the innovation challenge participants and their solutions and advance them within their companies.

The objectives of the project include: 1. Enhace the preservation, evaluation and use of olive genetic resources for improving the olive breeding and the delivery of new varieties

2. Leverage the information available through the development of a smart and user-friendly interface for the end-users 3. Predictions section: to provide information with regard to the predictable varieties’ behaviour in response to the different climate change scenarios – mapping the risked zones per each variety to avoid future economic losses.

4. We aim to determine and compare 30 olive genotypes’ behaviour in five different countries with very different environments and pedo-climatic parameters, in order to further develop breeding strategies taking into account the “environment-climate” variables. Furthermore, by knowing the Environment X Genotype interaction, we will be able to anticipate the climate change effects and select the olive progenitors for future breeding processes to obtain new resilient varieties. Specifically, we aim to: a. Determine and compare among 5 GBs the olive tree phenological parameters (blooming, fructification and maturation). b. Compare the varieties production and olive fruit and/or oil quality. c. Compare the

pests and diseases indexes – susceptibility scale in different zones and climates. d. Correlation of the aforenamed parameters with the climatic variables. e. Design breeding guidelines and strategies to face the future climate change scenarios.

5. Farmers section: i) to help determining the kind of variety to be planted in a specific geographic zone according to farmers’ necessities such as crop production, diseases risk, water availability and climate parameters; ii) to make available a free mobile app that will be able to determine the kind of disease that has affected the olive tree just by processing a mobile picture and to develop

online risk alerts for the rest of users by utilizing machine learning.

6. Breeders section: i) to provide in real time the best combination of olive progenitors for a specific breeding programme by implementing machine learning technology. ii) to provide a user-friendly but advanced mobile app that would be able to accurately identify through image processing an olive variety based on the morphological descriptors.

7. Development of a direct communication line between Germplasm Banks (GBs) and End-Users in order to ease the genetic material exchanging and speed up the collaboration between public and private sectors.

The remote sensing application in the mining industry can serve to monitor and compute a spatial-temporal model of the mining activities. It is the main inputs to an environmental impact assessment of mining sites at both local and regional level. On the other hand, the mining industry affects variety

influences on humans and wildlife habitats, and there is a need to estimate its cumulative impacts on the surrounding environment. Therefore, the information derived from multispectral and temporal remotely obtained data and imagery at the landscape level is the key data for developing designs and

decisions at local, regional, and national levels. This satellite/drone-based smart research will provide a suitable model for rural landscape changes in Mongolia and all over the world. Problem statement: Mongolia is a landlocked country and located between Russia and China; also, it has a large territory but a small population with scattered life. The consumption of Mongolia is a rural landscape, especially agriculture and pastureland for livestock. The last decade, mining and its related development projects are significantly affecting economically and environmentally to rural landscapes of Mongolia. Therefore,

the development of mining and its related infrastructure development is of great economic and social importance in Mongolia’s rural area. There are many studies on these booming effects in Mongolian development. However, there are few studies based on landscape ecology modelling for sustainable

development using geo-spatial data and modelling. In summary, there is a need to produce a more detailed study of Mongolia’s rural landscape changes in the last two decades by economic development, mining, by combining satellite imagery and integrating social data.

In a tropical country like the Philippines, it is expected that the country is densely vegetated and cloud cover is prevalent. In fact, at least twenty five percent (25%) of the Philippines is covered in dense vegetation while cloud cover ranges, on the average, from twenty to fifty percent (20- 50%) on a monthly basis. This makes prospecting for and mapping of resources, like renewable energy and minerals, on a national and regional level using Remote Sensing challenging.

This study aims to address this problem by using high temporal (i.e. to address cloud cover) and high spatial resolution (i.e. for high resolution mapping of vegetation cover) multi-spectral satellite imageries from ESA, particularly SPOT, WorldView and Pleiades, together with publicly-accessible imageries from Sentinel and LandSat to create energy and/or mineral resource maps through Geobotanical Remote Sensing (RS). Geobotanical RS refers to the use of satellite remote sensing indetecting variations or anomalies in plant growth (i.e. size, color, form, spectral signature, phenology, etc.) in response to their geologic environment. Geobotanical RS has been proven to be an effective technique to map out areas where minerals and energy resources are present in previous studies, particularly in relatively recent work in Japan and India. For the Philippines, similar efforts were also made, but these studies were mostly done in the late 1990’s/early 2000’s and used low to medium spatial resolution (i.e. LandSat 4/5 TM) only. While resource maps based from in-situ observations do exist in the country, these are outdated and are created mostly in the 1970’s and are presented as smallscale maps only.

The Gis-Co is a project that tend to invest in the data harvested from satellite through remote sensing and hydrological calculations to the welfare of residents in the Tunis city . this project at it’s first period is educational , the design zone would be in southern west of the gouvernerate of Ben Arous as the construction of a new industrial site has been taken place with a potential threat for the welfare of citizens . An initial mapping of the zone with a 30 meter spatial resolution free earth explorer maps is not sufficient for making precise and sophisticated calculation of the following parameters . This project tends to:

calculate the hydrological parameters of the Tunis watershed estimate the solid and liquid income within the 100-years life span relation to the reference terms of the industrial zones that built within Create an alert system for flood and fire hazard . Create a portal to navigate and explore the zone and it’s environmental characteristics . After rectification of the data and the creation of a automated A.P.I portal , the project would be open to new subject-study models like vegetation and the estimation of new parameters like vegetation and more. Thus , we wish that NoR would be our first demanded partner to achieve this dream and to sponsor our noble project that would save thousand of people and billions of investment by providing high resolution cards with 5 to 8 spectral bands .

The proposed project would investigate the use of Graph Signal Processing (GSP) for remote sensing problems. Observations provided by different satellites are often packaged as separate products with different spatiotemporal resolutions and coverage. With existing technologies, these differences in resolution are significant obstacles to combining multi-sensor (e.g., radar, visible, short-wave infrared) observations from various satellites. Motivated by these considerations, we will explore a graph-based analysis and visualization method that integrates publicly available observations from geostationary (GEO) and low Earth orbiting (LEO) satellites. The aim is to produce actionable datasets for applications where remote sensing data provide significant added value. Our main goal is to explore a technology that enables the integration of satellite data with in-situ measurements), while using relevant ancillary data (terrain information, for example). We propose an alternative to existing merging methods based on graph signal interpolation. Graphs are a good fit for processing signals that: i) lie on irregular domains and ii) are the result of physical processes where observed correlations can be attributed to the effect of multiple variables. Our novel GSP approach estimates high-resolution observations using graph-based interpolation on a graph constructed with weights that are a function of ancillary data such as altitude and temperature at high resolution. The explored methodology will construct graphs that allow us to combine multiple observations (from different satellites and at different resolutions) with relevant geoinformation. In this formulation, coarse observations are considered graph signals at specific nodes. In contrast, graph edge weights are chosen as a function of terrain information (e.g., distance, differences in altitude or similarity between neighbouring observations from another instrument).

The main goal of the EO4MASRISK is to fully utilize Sentinel-1 data, evolving from periodically updated ground deformation maps to early mapping and monitoring of landslide activity to increase urban resilience. EO4MASRISK service will help stakeholders and end-users to easily identify landslide moving areas and related potential impacts on built-up areas. The EO4MASRISK service functionality will provide the following information: Ground deformation time series; Ground deformation yearly velocity map; Landslide activity map (three levels, e.g., low, medium, high); Map of vulnerable elements at risk, e.g. buildings and infrastructure (three levels, e.g. low, medium and high); Datasets of the economic value of a property (available only for stakeholders); Potential damage map (three levels, e.g., aesthetic, functional, structural); Economic impact of a landslide on a building or infrastructure (euros/m²).

In this research project, we would like to explore areas of land subsidence with the P-SBAS on-demand processing service on the GEP. The objective is to obtain land displacement velocities for watersheds that are over-exploited and need modeling-based approaches to mitigate the risk of land subsidence. Land subsidence data obtained from the InSAR processing on the GEP is going to be used as calibration data for a geomechanical model, which will be coupled with a groundwater flow model. Therefore, the acquisition of P-SBAS processed InSAR data from the GEP is critical for our project. Foreseen results are as follows:

– Develop an innovative methodology for the hydrogeological characterization of large-scale aquifer systems using low-cost and nonintrusive data such as satellite-based Earth Observation (EO) techniques.

– Integrate advanced EO techniques into numerical groundwater flow and geomechanical models to improve the knowledge about the current capacity to store water and the future response of aquifer systems to natural and human-induced stresses.

– Enhance the knowledge about the impacts of agricultural and tourism activities on the water resources by quantifying the ground deformation during the monitored periods.

The project is focused on the creation of several functionalities that are useful in the agricultural sector. Specifically, these features will be useful in high-value crops like almonds, olive-grooves, pistachios and avocados, between others.

Giving to agronomists the information related to the canopy trees without the necessity of flying drones can make the change in the following years to better monitor these type of crops. The objectives are:

– To create an algorithm for automatic detection of canopy trees.

– To create an algorithm for automatic detection of vegetation covers.

– To give insights about the healthy of every tree in the crop.

– To create variable rate application maps for canopy tree crops.

The main beneficiaries of these functionalities are farmers, field technicians and agronomist engineers. Specifically, the technicians and the agronomist can improve their current work thanks to the insightful maps that could be created with the algorithms explained. Tasks like the irrigation or fertilization of the trees can be improved in order to produce more while reducing the use of natural and artificial resources. For example, knowing the exact healthy state of every tree we can create variable rate application maps so that the machinery can apply just the exact quantity of product that is needed. The agronomists and technicians can be single workers or they can make part of technical advisory services from cooperatives, fertilizer companies or agrotech companies which are already in the market. The maps will be consumed through a web application platform called Graniot where the beneficiaries can create their own insightful maps or ask for specific ones. The platform will facilitate the way beneficiaries access to the most innovative satellite technology: very high resolution images of 50x50cm and 30x30cm per pixel. The maps can also be consumed through an API for technological companies.

Established in 2019, Albo-Climate is combining geospatial modeling and AI expertise to provide state-of-the-art remote sensing solutions for carbon-stock and ecosystem monitoring across diverse ecosystems. Albo’s technology solves the manual, expensive, hardware-dependent in-field measurements common in the carbon-credit market. We are creating a new paradigm of transparency and scalability in Nature-Based Climate Projects, by mapping land-use and carbon-stock changes at high accuracy and on a per-pixel resolution. Our innovative solution has already received the Solar Impulse Prize and Official Concept Note Approval from Verra, the largest registry for voluntary carbon credits in the world.

Albo Climate’s cutting-edge, automatic, remote sensing platform allows landowners of any size – both public and private – to easily enter the carbon credit market and start selling credits from their land. Albo’s solution also enables project developers seeking to participate in the carbon credit market to easily and efficiently initiate a Nature-Based offset project and monitor their project development. Moreover, Albo’s technology allows project developers to detect major threats affecting the project site in near real-time such as deforestation, forest degradation, crop diseases, and flooding. By removing the main structural, technological and financial barriers, Albo aims to scale up the voluntary carbon credit market and unlock its full potential to mitigate climate change.

The key project aim of this master thesis is to apply state-of-the-art deep learning methods on satellite

imagery to detect bi-temporal changes in land usage in Germany. Achieving this research aim serves

two purposes: firstly, it showcases how state-of-the-art deep learning techniques can be used to analyse

remote sensing data for a large geospatial region (the whole of Germany) that would not be possible to

analyse in a manual fashion, and secondly, it sheds light on the key research question addressed in this

work:

Research question: How did the land cover and land usage change due to human and non-human

influences between two points in time?

Three research objectives support the research aim and the key research question: i.)

Train a deep learning model to be able to classify satellite images into separate land-use classes with a

classification accuracy that is significantly above a naïve (baseline) classifier when evaluated on holdout test data. ii.) Acquire suitable satellite image(s) from Germany at two distinct points in time that

can be meaningfully compared (i.e., where the weather and cloud conditions are sufficiently similar to

warrant comparison) and pre-process the data to be handled by the trained deep learning model. iii.)

Classify the satellite image(s) at both points in time and compare the land-use classes, both in

aggregate and individual changes.

This study generally aims at investigating what are the most efficient IWRM and IRBM strategies to develop and implement that will ensure social and hydroecological resiliency of the Sanaga River Basin in the context of climate uncertainties? The overall purpose of this dissertation is to elaborate predictive hydrological machine learning models and roadmap schemes for decision makers that will enable them to enhance water security, water access and resilience in managing fisheries and energy resources of the Sanaga River Basin in the context of climate change and increasing water scarcity risks; and to propose adapted management strategies as mitigative solutions. Specifically, we will:

• Assess current Land Use and Land Cover of the Sanaga River Basin; describe how it has evolved during the last decades and predict future physiographical states from the climate trends according to the climate models scenarios RCP 6.5 and RCP 8.5;

• Investigate the hydrological response of the Sanaga River Basin and predict possible extreme hydrological events based on some IPCC, CMIP6 and CORDEX climate models;

• Determine the hydrological resiliency of the Sanaga River to sustain current and future National projects for hydroelectric development;

• Assess and model the ecohydrological response of the Sanaga River Basin, and predict future trends in hydrological and ecological indicators;

• Assess the Water Policies and Governance of the Sanaga River Basin in the new framework of decentralisation and regional competencies transfer; and propose an interregional Master Plan for IWRM in compliance with SDG 6 criteria and others real options criteria.

The objectiv eof the project is to compare state-of-the-art and Hydrocoastal products in the Northern Adriatic Sea against in situ measurements.

The main objective of the project is to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. There are specific objectives for each of the Coastal Zone and Inland Water domains, and particular Technical Challenges to be addressed. However, one of the key aims is to link together and better understand the interactions processes between river discharge and coastal sea level. Key outputs are global coastal zone and river discharge data sets, and assessments of these products in terms of their scientific impact. The aim of the project is to study new approaches for processing SAR and SARin data from Sentinel-3 in coastal zone and inland water. Various approaches are evaluated in selected test zones and the most promising processing schemes are identified. The chosen approach is then implemented to generate global coastal zone and discharge datasets and the scientific impact of these products is evaluated in few case studies.

Existing methodologies for monitoring the temporal changes in forest areas have poor performances in high altitude and sloping areas such as Alp mountains or Iberian mountains. That is why, in such hilly regions, prediction of tree cover density or decision on forest existence and forest type are highly error prone, as already demonstrated by [Dostálová et al.] [Cremer et al.]. Thus, our aim in this research is to develop a robust tree cover density prediction algorithm by combining the earth observation data with topographic information and climate categories. Existing European wide forest change detection methodologies are mostly rely on classical statistical measurements and thresholding techniques, however in our research, we are aiming at using advanced machine learning techniques such as variants of UNet and Transformer architectures.

As an output of our research, we will predict more robustly which parts of European forests are subjects to degradation and how severe those changes are. Thus, our contributions will help decision makers to take preventive measures for protecting the forest areas and recovering them in the long term.

The project intends to bring information about new and current fly tipping and illegal landfills over the territories. We do that by combining very high-resolution optical images with state-of-the-art deep learning algorithms. We integrate the results of our analysis in a geographical database, and we build collaborative functions to help stakeholders coordinate their action to evacuate the waste and wipe the polluted sites. Our end users are public authorities that struggle to understand the phenomenon and the spatio-temporal patterns due to the lack of a platform centralizing all the location, date of images, approximated volume, growth over images acquisition and actions that have been undertaken on the locations.

The project implies to task AOI regarding the level of the customer (region, department), requiring between 8 and 12 images a year, depending on the feasibility, available bandwidth and cloud coverage.

The platform is available for end users without limitation of time or functionalities, and they are aware about the last date the predictions have been added to the platform. Which depends on the acquisition

process.

Project objectives: Scientific VM template to provide continuity after esa365 migration

The objective of the project is to assess the health conditions of different crops such as coffee, cotton across various plantations in Africa, South America and Asia by using high resolution satellite images. The project aids the farmers by providing timely remedies to improve the yield. This helps the farmers to take rapid decisions on various remedies to be considered for effective yield. The solution will help upon the following functionalities:

– Crop Health Assessment for Various Farms: For precise and timely production forecast, crop health assessment is a vital component. Using a variety of image processing techniques, the health of the plants is determined from the satellite images. Plants are classified into healthy and unhealthy plants, and the farmer is informed of the health conditions of the crops across the farm.

– Remediation Steps to be taken by farmers: The agriculture industry faces numerous difficulties that needs timely resolution. The project proposes remedies for the identified health conditions. These remedies can be applied for improving the yield across the plantations

– Accessing Yield of various crops: From the satellite images captured from the plantations, the project can derive an approximate yield of various crops grown. This can help the farmers understand their overall productivity across farms. The predicted yield can help the farmers to make effective global decisions. The project utilizes high resolution satellite images to achieve these functionalities. The results of this research are used to make wise decisions in a timely manner to increase yields throughout the plantations across various geographic regions.

“Floods are increasingly affecting many countries globally, and in particular, urban areas, over the past decades. In several Global South cities and particularly in Sub-Saharan Africa, they are the most frequent disaster. The gradual transformation of natural soils into impervious surfaces has led to a low infiltration capacity and a growing surface runoff volume. This change, which reflects urbanisation, has become uncontrolled due to poor control over land, rapid housing production, urban growth, and cities’ fragmentation legacy inherited from the colonial period. Over time, so-called “”informal”” settlements have been set up in spaces where the risk of flooding is high. This mechanism tends to weaken vulnerable populations, including the poor and people “”trapped”” in these areas. The adaptation measures adopted by both the population and other actors (governmental* and non-governmental) are often temporary and insufficient. Given the population growth projections for these cities, there is a risk that future urban expansion amplifies the exposure of vulnerable groups to flooding in the coming years.

The overarching question addressed in this research is: “How to foster the resilience of informal settlements located in flood-prone areas?” The objectives are (i) to develop simplified models of pluvial floods considering climate change, and applicable in data-poor environments such as Global South cities, (ii) to analyze the drivers and challenges of urban densification and expansion, and informal settlements development on the one hand and vulnerability to flooding on the other hand, and (iii) to develop a hydrosocial model dedicated to informal settlements in flood-prone areas. We will synthesize the research results in an integrated approach for the cumulative and long-term adaptation of informal settlements exposed to flood risks.”

The Trans Adriatic Pipeline (TAP) is a pipeline that extends from the Turkey-Greece border, crosses the northern Greece mainland and southern Albania, and ends up in southern Italy through the Adriatic Sea. Along this pipeline, several areas of geohazards have been identified posing a threat to the integrity of the pipeline. These geohazards are related to slope failures i.e., landslides and rockfalls, liquefaction and subsidence. This project focus on landslides that represent the most significant hazard for TAP due to the fact that can induced severe damages to the pipeline in case of triggering of the mass movement. In particular, the goal of this project is twofold; application of the InSAR technique in order to identify areas of active slope failures along the TAP and monitoring them in order to evaluate the landslide displacement rates. Furthermore, the obtained results by the application of InSAR will be compared to an existing inventory of landslides along the TAP corridor and validated based on the outcome of inclinometer systems that have been installed within the landsliding mass in order to measure the displacement along the sliding surfaces.

The project objectives are to transform agriculture into a source of passive income for farmers and create an ecosystem based on AI using the Farming As A Service (FAAS) concept.

Farmers being part of this ecosystem will benefit from live customized agricultural plans and actions for their farms. The agricultural plan and actions modify according to crop evolution, the impact of agricultural technological activities, meteorological events, and pest/plant disease events. Starting with data gathering from satellites and interpretation from our agriculture specialists will offer farmers recommendations on what actions they need to take to protect their crop, what products they should use and in what quantity to maximize their yield and save money. This way, products will be applied rationally using Variable Rate Application, soil will be protected and regenerated by implementing agroforestry and carbon farming methods.

• Measure the performance of implementing agroforestry in conventional agriculture;

• Measure the performance of carbon farming methods;

• Study the overall temperature reduction of the crop by implementing agroforestry and carbon farming;

• Study the reduction of evapotranspiration of water from plants and soil by implementing agroforestry and carbon farming;

• Reduce the use of chemicals products for agriculture;

• Reduce overuse of water;

• Reduce labour with agricultural machines;

• Obtaining sustainable agriculture.

From the result of this study will benefit:

• Farmers, because they save money and resources;

• Consumers, by eating healthier food because the plants will be closer to natural development, with minimum intervention with chemicals;

• Overall population, because our actions will reduce the carbon footprint of businesses related to agriculture.

The results directly impact the economy, the population’s health and climate change, so that they will be available from day one of our launch on the market. The results will improve as our ecosystem improves.

Objective of this project is to examine how the railroad system impacts the living environment in a certain region, by using artificial intelligence and land classification.

LAYERS is an AgTech platform currently being used by more than 3.000 users around the globe for all kinds of crops in four main products: SatTech2.0, SatPred, SoilTech and DroneTech. This platform evolved from drone-only to multi-input mainly for the operative costs and complications of the drone operations. However, drone are still being used in some “surgical crop-specific” use cases such as tree counting, weed or disease detection and monitoring.

SatTech2.0 and SatPred products use as spatial data Sentinel-2 and Sentinel-1 data accessed through SentinelHub.

The objective of “LAYERS HD Upgrade” is to explore, implement and test with real users higher resolution images in both intensive (e.g. orange trees) and extensive (e.g. sugarbeet, corn) crops.

PlanetScope HUM will be implemented in different crop types and both spatial and analytical results will be presented at least using NDVI but most probably other indexes such as NDRE. All these data will be available in LAYERS.

Rest of satellite sources will be explored in at least a fruit and an extensive field to evaluate the value that these products may have to the end-users. Imaging will be available in LAYERS.

All of these demonstrations will be offered to LAYERS users with no additional cost.

LOGS project aims at investigating the past, recent, and future evolution of a hundred Local Glaciers (LG) near the Greenland settlement of Sisimiut. Greenland LGs, namely those glaciers not linked with the Greenland Ice Sheet (GrIS), are often overlooked by the research community in favour of GrIS, despite their higher sensitivity to climate change. LGs contribute for 15% of Greenland’s ice loss and have an important role in the local landscape providing hydropower, serving as recreational terrain and affecting local ecosystems. Understanding the past, recent and future evolution of such landforms is therefore not only fundamental to evaluate the cryosphere’s response to global and local warming, but also to inform the locals on the evolution of their natural landscapes and related practical effects.

The scope of the project is the determination of long-term post-seismic ground surface movements in L’Aquila (Italy) after the devastating earthquake of Mw 6.1, on April 6, 2009. This earthquake highlighted the incomplete understanding of the geology of the area, in particular the Quaternary continental deposits and active tectonics, which caused the Paganica fault system to be ignored by researchers. Coseismic seismological and geodetic data converge in modeling a NWstriking, SW-dipping, normal fault (length ranging between 12 and 19 km) as the causative fault of the 2009 earthquake (Chiaraluce, 2012 and references therein). Soon after the earthquake, a fault bounding to the east of the Middle Aterno Valley, along which primary coseismic ruptures, was interpreted as the surface expression of the modeled fault (Boncio et al., 2010; Emergeo Working Group, 2010; Falcucci et al., 2009; Galli, Giaccio, & Messina, 2010; Vittori et al., 2011). The scientific production concerning seismological, geodetic and geologic coseismic data is focused on the identification of the seismic sources. In particular, the geodetic data is focused only on co-seismic and immediate postearthquake. **The aim of our project is to investigate post-earthquake movements to understand the evolution of the fault system by using a novel approach derived by the calibration and integration of ESA Sentinel1 InSAR with GNSS and with high-resolution leveling networks measured in 2009 and 2018. **

The study area is characterized by vertical movements in both lowering and lifting that go from -2 mm /a up to 5 mm/a. The research will provide unique and important geophysical information concerning the dynamic of the fault system after the earthquake of 2009. At the same time, we will test a new method for the integration and calibration of InSAR with GNSS and with high-resolution leveling networks.

With nearly half a billion people living in the close vicinity of active volcanoes around the world, the volcanic threat nowadays represents a major subject of global societal issues focusing on both the population protection / prevention and health. The diversity of acquisition systems, as well as the availability of large quantities of data, make the use of space imagery particularly suitable to meet the technical needs raised by the dynamic monitoring of continental surfaces.

Thanks to several decades of development, optimization and exploitation, the SAR interferometry has largely demonstrated its potential for excellence and today constitutes a proven, relevant and high-performance technique. This technique reveals in particular a significant potential in many fields of application addressing natural hazards as related to population prevention

(earthquakes, volcanic eruptions, landslides, floods, etc.), agriculture / forestry (deforestation, fires, phenological evolution, structural modifications and textures of the soil and plantations, etc.), geodynamics (phenomenon of subduction, subsidence, etc.) or questions relating to town planning (modification of the urban landscape, exodus, etc.).

For that reason, the primary objective of our project focuses on the assessment of the dynamical monitoring of explosive volcanoes based on an approach combining Machine Learning methods and Differential SAR Interferometry (DInSAR) products.

The Norwegian Computing Center, the Danish Meteorological Institute (DMI), the Technical University of Denmark (DTU), Polar View, Nansen Environmental Remote Sensing Center (NERSC) and ESA (European Space Agency) have created a sea ice challenge, intending to bring together ΑΙ and Earth Observation players to address the challenge of “automated sea ice mapping” from Sentinel-1 SAR data.

Manual ice charting from multi-sensor satellite data analysis has been, for many years, the primary method at the National Ice Services for producing sea ice information for marine safety. Ice analysts primarily use satellite synthetic aperture radar (SAR) imagery due to the high spatial resolution and the capability to image the surface through clouds and in polar darkness, but also optical imagery in clear-sky and daylight conditions, thermal-infrared and microwave radiometer data from, e.g. AMSR2. Ice analysts mention the spatial resolution of microwave radiometers as the primary limitation of using data.

The traditional manual ice charting method is time-consuming and limited in spatial and temporal coverage. Further, it is challenged by an increasing amount of available satellite imagery, along with a growing number of users accessing wider parts of the Arctic due to the thinning of the Arctic sea ice.

Automating the time-consuming and labour-intensive sea ice charting process can provide users with near-real-time sea ice products of higher spatial resolution, larger spatial and temporal coverage, and increased consistency. Convolutional Neural Network (CNN) has excellent potential in automated sea ice prediction in satellite images. However, automating the process on SAR data alone is challenging. SAR images show patterns related to ice formations, but backscatter intensities can be ambiguous, complicating the discrimination between ice and open water, e.g. at high wind speeds. The training dataset made available in this challenge contains Sentinel-1 active microwave data and corresponding Microwave Radiometer (MWR) data from AMSR2 to enable challenge participants to exploit the advantages of both instruments. While SAR data has ambiguities, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water.

The objective of the AutoICE challenge is to advance the state of the art of sea ice parameter retrieval from SAR data resulting in an increased capacity to derive more robust and accurate automated sea ice maps. In this challenge, we aim to push forward the new capability to retrieve multiple parameters, specifically, sea ice concentration, stage-of-development and floe size (form).

More extreme, prolonged, and widespread droughts are accelerating tree mortality across biomes. Our ability to mitigate these changes depends on our ability to predict when and where mortality events are most likely to occur. This is complicated by the fact that, even within the same species, genetic variation drives phenotypic differences in ecophysiology, which result in populations having differential mortality risk under similar conditions. Thus, forecasting a species’ probability of mortality under drought can be improved by understanding its genetic and phenotypic variation across landscapes.

Resource-intensive methods requiring field campaigns and laboratory analyses have historically prevented scientists from gathering spatially explicit datasets describing genetic variation at large scales. Remote sensing and machine learning, however, present the opportunity to efficiently generate continuous, high-resolution maps of genetic variation across landscapes. Early efforts to map intraspecific genetic variation using remote sensing have yielded promising results, but have focused on small areas using hyperspectral and high spatial resolution imagery from drones and aircraft. At the current moment, scaling up mapping of intraspecific genetic variation to broader spatial and temporal coverage will depend on multispectral satellite data.

The output of this study will be the largest continuous map of aspen ploidy to date, enabling consideration of ploidy in a macroecological context. If predictions have low accuracy at the extent of the entire state, I will be able to instead determine the spatial scales at which spectral signals of ploidy level are consistent. Even at smaller scales, e.g. national forests, findings would have useful ecological and land management applications. I will ultimately use the output map to test the hypothesis that ploidy level predicts mortality following drought. If true, we will identify areas that may be important seed sources for climate-resilient restoration efforts, as well as high-risk areas that should be prioritized for future management.

The main objective of the study is to use non-parametric machine learning classifiers to map informal settlements of Old Naledi from Sentinel-2 data and Google Earth data. To achieve the main objective, the study will implement the following specific objectives: Extract buildings from European Space Imaging/Maxar WorldView and Google Earth data features using CNN and RF. Examine the significance of GLCM texture in the extraction of buildings in informal settlements. Determine the accuracy of extracting informal buildings from European Space Imaging/Maxar WorldView, and Google Earth using ground-truth data

“The Wild Intelligence Lab project was launched in January 2021. Since then, we have been able to recruit 40 qualified students, doctoral candidates, and postdocs from the fields of computer science, engineering, and physics for our project. Wild Intelligence Lab enables objective decision-making in the sustainable development of conservation driven by data. We work on solutions for the protection of threatened ecosystems. We count animals and plants on drone images. Using artificial intelligence, the software recognizes giraffes, rhinos, and antelopes, records animal populations, and calculates the amount of food available. Using satellite imagery, we evaluate the health of trees. By analyzing this data, our work can be used to develop strategies to protect threatened ecosystems. Currently, we are developing our software in close collaboration with the nature reserve Kuzikus in Namibia. Doing so, we can start with a minimal viable product and continuously add software features, tailored to the needs of conservation experts. For us, scalability is key. Thanks to our software architecture, we only need new data from unknown wildlife to train our algorithms for quantifying animals and vegetation, providing wildlife experts with transparency and protecting endangered plant and animal species in the long run. In addition, Dr. Friedrich Reinhard and Berend Reinhard, head of the Kuzikus nature reserve in Namibia, are founding members. Our members are involved voluntarily and some of them are writing theses in the form of master’s theses about the project. Currently, we are working closely together with our partners Kuzikus, Drone Adventures, LiveEO and SAVMAP. We want to use the high-resolution satellite data from Pleiades to calibrate our model and make the algorithms more robust for new areas. In the future, we aim to use the model for new projects, such as the Black Rhino Habitat Suitability Analysis. This is intended to identify new, suitable and safe habitats for black rhinos in Namibia. This should enable the breeding programme for this endangered species to continue. Further information is available at the following link:

https://wildintelligencelab.com/black-rhino-habitat-use/”

MedEOS is a research project that aims to develop, implement and/or generalize methodologies using Earth Observation (EO) to acquire coastal water quality information about nondirectly remotely measurable parameters. It is part of the ESA Mediterranean Sea Regional Initiative within FutureEO-Segment1 ESA programmatic line (2020-2022) and aims to develop and produce high-resolution, gap-free maps of experimental EO water quality products by employing data fusion techniques to combine the high temporal resolution of S3-OLCI and high spatial resolution of S2-MSI data. Moreover, MedEOS will develop, implement and demonstrate a methodology to produce an

extensive tracking of river plumes in Mediterranean coastal waters with the use of EO products.

MedEOS is a research project that aims to develop, implement and/or generalize methodologies using Earth Observation (EO) to acquire coastal water quality information about non-directly remotely measurable parameters. It is part of the ESA Mediterranean Sea Regional Initiative within FutureEOSegment1 ESA programmatic line (2020-2022) and aims to develop and produce high-resolution, gap-free maps of experimental EO water quality products by employing data fusion techniques to combine the high temporal resolution of S3-OLCI and high spatial resolution of S2-MSI data. Moreover, MedEOS will develop, implement and demonstrate a methodology to produce an extensive tracking of river plumes in Mediterranean coastal waters with the use of EO products.

On August 7, 2020, the Mila region was hit by a moderate earthquake which caused a huge

landslide that swept away 1/4 of the city of Mila and caused the distruction of buildings and important

infrastructure. With the evolution of the space technology, this geological event can be measured with

precision, and it is possible to determine the boundaries of the affected areas while calculating the slip displacement using only two high resolution images (before and after the event). The obtained results (displacement maps) will contribute to the the understanding of the damage caused and will be used to compare radar and optic data.

The project aims to highlight the implications of mining and quarrying activities on the biophysical environment. This will entail spatial mapping, Land Use Land Cover analysis of satellite images to detect changes on land structure and vegetation communities. Impacts of these changes will be analyzed on the hydrology and water resources because the ecosystems in which large scale exploration activities are occurring have water channels passing nearby.

The results will be spatial maps of the extent of all mining and quarrying activities, change detection maps dating back to years before extraction activities began and 3D earth terrain models to translate changes in land structure and vegetation communities on the hydrological system. Mapping of the spatial extent and nature of mining and quarrying activities will be done through digitization of satellite images and verified though field surveys. Detection of landscape structure will be done through classification of satellite images (supervised classification and change detection algorithms). NDVI algorithm on the other hand will come in handy in detecting changes in vegetation communities. Satellite images furthermore can be helpful to detect physical changes in water sources such as water quality parameters of suspended solid matter. This will be supported by lab analysis data by the water authority in Kenya. The research will inform the government, exploration companies, and the communities on the implications of mineral and stone exploration activities in the County. In addition to adding to the knowledge in the use of satellite images for monitoring man’s activities in the environment.

Air pollution is caused by a combination of ~78% nitrogen, ~21% oxygen, ~0.9% argon and the remaining elements include carbon dioxide, methane, water vapour, hydrogen, and other trace elements emitted from factories and motor vehicles that burn fuel. The atmosphere is a delicate balance of these gaseous

elements and particles. Any imbalance, even in little extent can be inconvenient to living life forms including animals and crops.

There are different tools and techniques to study the air pollution problem. Remote sensing has been widely used for air quality studies since sum of the effects from the ground and atmosphere signal can be observed by the satellite sensors. Researchers have been using PM2.5 and PM10 to analyse the air

pollution situation using remote sensing for different countries. Our objective is to study the airpollution problem at delhi city and other metropolitan cities:

1. Modelling of PM2.5 and PM10 using Earth observation datasets

2. Fire detection model using Satellite imagery for agriculture problems

3. Development of an algorithm using an open source software interface R

4. Identification of lockdown effect during Covid-19 on atmosphere condition in Capital city of India.

We have been doing the basic analysis using ground observation datasets on cities of India. 22 of the 30 most polluted cities in the world are in India, and almost 99 percent of Indians breathe air that is above the WHO’s defined safety limits. According to the past analysis 76% of Indians live in places that do not

meet national air quality standards. In 2017 one in eight deaths in India was attributable to air pollution additionally average life expectancy of a child is reduced by at least 2.6 year.

Along Chile’s entire ~4000 km coastline, oceanic tectonic plates (the Nazca and Antarctic plates) subduct under the South American continent, repeatedly causing great to large earthquakes. Chile is thus a natural laboratory to better understand the processes related to large earthquakes. The PRECURSOR project is an initiative funded by the Chilean Ministry of Science to investigate the mechanics of slow earthquakes and their relation to precursory signals. For this purpose, we use deployed a pioneering experiment in Chile with a dense distribution of continuous 30 GNSS and 80 seismological stations (https://www.precursor.cl). Our project seeks to improve the detection of interrelated mechanisms controlling the failure of faults, to resolve spatiotemporal relationships between frequent small earthquakes and transient deformation, and provide new insights into the genesis of earthquakes.

The United Arab Emirates (UAE) are located on the southern side of the Arabian Gulf, at the north-eastern edge of the Arabian Plate. The bedrock geology is well exposed in the Hajar Mountains and the Musandam Peninsula of the eastern UAE, and along the southern side of the Arabian Gulf west of Abu Dhabi. The tectonic history of the UAE include the opening of the Red Sea by the Middle to Late Eocene, culminated the start of the NE tilting. The movement of the Arabian crust under the Iranian crust (Subduction Zone) is about 3 cm/year. Thus, the broader area of UAE is considered relatively active and the need to address various geohazards is necessary. The availability of satellite-based techniques, an specifically InSAR, offer the opportunity to measure surface displacements relevant to geohazards, being essential for improving our understanding of the phenomena related to both natural (subsidence, landslides, etc.) and anthropogenic hazards. The significance of these phenomena in terms of risks – to both people and infrastructures – is considerable; the resulting surface displacements must therefore be surveyed in order to prevent potential damages (in the case of anthropogenic hazards, surveillance may even be required by the regulations in force). The main objective of the current work is to address relative sea level changes and coastal vulnerability over entire UAE territory. A precise (millimetric precision) and comprehensive measurement of the ground motion along the coast is therefore required to fully characterize the sea level rise. For this purpose InSAR appears as the most effective tool to link the sea level estimates at global and regional scales to the local scale.

We intend to monitor past (i.e. over recent years) changes in glacier velocity, to establish whether glacier velocity increases prior to volcanic eruptions. The results will be of interest to the scientific community, but might also help improve volcano monitoring and associated hazard prediction.

The aim of this research project is to use the MPIC-OPT-ICE tool to monitor the surface velocity of glaciers that occupy active volcanoes with a particular focus on Mount Wrangell (Alaska), Mount Veniaminof (Alaska) and Volcan Peteroa (Chile). Preliminary results are planned to be made available by June 2022.

The Einstein Telescope (ET) is a proposed underground infrastructure to host a third-generation gravitational-wave observatory. There are currently two candidate sites to host it: one is located in Sardinia, in a favourable geological context, and the other is in the Meuse-Rhine Euregion. Site-characterization studies are underway towards the site selection, which is expected in 2024. The research aims to evaluate the sardinian candidate site by integrating remote sensing techniques with geological and geophysical data. In this framework, a fascinating aspect relates to surface deformation evaluation through the PSI technique with SAR data. Recent years’ Sentinel-1 data from the Copernicus program represents a good opportunity to check recent crustal movements: they are supposed to be very little accordingly with a particular Sardinian geological asset. A first analysis has been performed locally at my university workstation with Snap2Stamps methodology: the next step is to expand time series observations and to fix a “zero” for further investigation. Unfortunately, SAR data processing is very expensive in terms of time and resource consumption, so the possibility of using the SNAPPING service is an excellent opportunity to achieve my research scope. My research result will be published in dedicated scientific publications in an open-access format to be directly available to project developers and the scientific community. The impact estimation is very high due to the substantial interest in this strategic project.

The Junior Academy of Sciences of Ukraine is a state-funded extracurricular educational system that develops and implements methods of science education. the Junior Academy of Sciences of Ukraine received the status of Category 2 Science Education Center under the auspices of UNESCO and joined the network of Copernicus Academies. In 2012, a new section, Geographic Information Systems (GIS) and Remote Sensing of the Earth (RS), was established at the Kyiv branch of the JASU, which is supervised by the GIS and RS Laboratory. Today in Ukraine there is a delicate civil and governmental situation, and high-precision satellite images are important data for monitoring and assessing its status. We are already using Copernicus data that is publicly available, but high-precision satellite imagery will increase the amount of information for students and teachers for identifying the state of civil infrastructure. We have experience in conducting both national and international educational events for students and teachers. We will use this experience for the organization of international webinars.

Like most arboviruses, RVF is driven by a complex interaction of mosquito vector populations and vertebrate hosts in different habitat types under varying environmental conditions. During previous outbreaks, primary key vectors of

the RVF virus were identified, but the limited understanding of their ecology in diverse ecological zones and the interplay with the nomadic pastoral systems along the major livestock movement routes are unknown. For these reasons, this study seeks to analyze cattle movement routes to understand resource utilization, i.e., where they spend a lot of time grazing and how they often utilize that area, water points, and distance moved between forages. This will provide new insights on the exposure of cattle to mosquito biting. Analyses of cattle movement pathways (trajectories) also integrate each cattle location into the larger context of the spatial distribution of the population and changing environmental conditions. A perspective that has not yet been used in understanding RVF outbreaks, so the information obtained from this research will be valuable to the science community and fill a research gap on the role of cattle movement in the spread and maintenance of RVFV.

It is also envisaged that tracking animal movement will permit the identification of areas where the introduction or amplification of the disease could potentially occur due to a high density of RVF vector populations. This will contribute to the understanding of RVF epidemiology and present opportunities for strategic disease prevention.

Data extraction results will be regularly be posted by our association on our website (currently under development), our Instagram and on our university’s private network, and we plan to collaborate with at least another Engineering school to implement our data into AI algorithms to accurately predict natural phenomena. We plan on developing our very own meteorological station based on the data we will extract. Finally, we plan on participating on SentinelHub’s custom script competitions and on those of many other institutions. We believe this makes us a research and educational project which has the potential to reach a large number of beneficiaries.

On behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ), GIZ implements the project of “FAIR Forward – Artificial Intelligence for all”. FAIR Forward strives for an open, inclusive and sustainable approach to AI on an international level. The project’s objective is to augment the prerequisites necessary for local AI development and use across its six partner countries (Rwanda, South Africa, Uganda, Ghana, Kenya and India). As part of the area of capacity development and removing entry barriers to AI, FAIR Forward has partnered with the Rwanda Space Agency (RSA) and the German Aerospace Center (DLR) to sustainably enhance capacity building efforts on Machine Learning for Earth observation (ML4EO) for Rwandan practitioners and support innovative ML4EO applications to address development challenges. Thereby, the program aims to lay the foundations of a prospering ML4EO ecosystem in Rwanda.

The goal of the openEO platform project is to develop a cloud-based system for large-scale analysis of Earth observation data via easy-to-use programming libraries (Pyhton, JavaScript) and clients familiar to data scientists (Jupyter Notebooks, R, WebEditor). The project builds on the heritage of the H2020

project openEO and is now moving to an operational platform offering openEO as a service, embedded in a unique federated European architecture.

The development of the platform is driven by different use cases, all aiming at extending the capabilities of the platform through additional functionality linked to real-world examples. As foreseen in the project tender, part of the data access costs (in this case the requested commercial data) should be covered by additional funding.

Commercial data part:

The requested commercial data is used for a use case to determine fractional canopy cover (FCC). This parameter is important for monitoring changes in forested areas and is a key input parameter for many environmental and ecological models. In this use case, we foresee the prediction of FCC for a 900.000 km2 area in central Europe. The prediction will be based on Sentinel-1 and Sentinel-2 features such as polarization maps, individual bands or vegetation indices. The role of the Very High Resolution (VHR) data will be the delineation of forest and non-forested land cover at the pixel level in several

test sites.

The added value of the VHR data is the very high spatial resolution, which allows a detailed distinction of forested and non-forested areas in detail in many pixels at a small scale. Based on the FCC obtained for VHR pixels the FCC percentage is spatially scaled up to individual pixels of medium resolution sensors. In the use-case, the binary forest information is applied to a 20×20 m Sentinel-2 grid. Another valuable piece of information for FCC prediction over a large area is temporal resolution. Differences in phenology, forest cover density, and tree species benefit from multiple images or time series over the same site throughout the year. This strengthens the prediction of forest presence within each VHR pixel and increases the data density for the regression model and/or allows modelling of seasonal behaviour.

ORCS is an application based on Artificial Intelligence aimed to detect features like ships and airplanes over EO optical currently supporting RACE Project (https://race.esa.int/), a joint initiative between ESA and European Commission for the provisioning of several economic indicators. It has been employed a Faster RCNN architecture due to its capability to provide fast and reliable results in the object and features detection. The activity, started as internal prototype at the very beginning of the 2020 pandemic situation, it has been piloted as in-kind contribution to ESA and since September 2020 it is into operations running within the EDC platform and supporting indeed RACE project.

Earth Observation missions generate a large amount and variety of satellite data, treasure troves waiting to be fully exploited but currently underused because their data format, volume and complex geometry constitute a barrier for many users. To help remove this barrier and foster data synergy exploitation, open tools such as the Ocean Virtual Laboratory were developed with ESA support, making data discovery, access and analysis a rather easy task for science users. The aim of the OVL-NG study is to: Evolve, maintain, operate the ESA Ocean Virtual Laboratory Next Generation according to User needs.

The main technical objectives of this project are to prolong the ESA/Copernicus data visualisation and promotion activities started in OVL and S3VIEW for 24 months, to improve tools and services based on user feedback and to explore ways for improving the sustainability of these services in the long term.

Major changes are required in the core of the SEAScope application to allow it to stream data from a remote source, such as a Cloud, a DIAS or a datacenter. These developments are mandatory to facilitate the visualisation of large quantities of EO data and to make the application more attractive for users who need to explore and analyse these data without downloading full data sets. Design and implementation of user-requested features will be intertwined with the development of these core evolutions to achieve the most satisfactory outcome. The sustainability of existing and upcoming services can be improved by reducing the amount of time required to operate them and by optimising both usage and cost of the infrastructure resources. A panel of clouds and DIASes will be studied to get a clear view of the offers available to host services similar to OVL-NG. The processing system that feeds the online portals will be optimised to consume as little resources as possible, to perform more monitoring tasks and to handle minor issues autonomously so that operating the backend of OVL-NG involves less human interventions.

I am an investigative reporter with Noteworthy.ie in Ireland and I am currently carrying out an investigation into unlicensed peatland extraction in Ireland. Our investigations are published on our website and simultaneously on TheJournal.ie, the largest native Irish online news outlet with 550,000+ average daily users, aged largely between 24-55, with a 50/50 gender split. As we publish in the English language, we additionally attract a small audience from the US, the UK and beyond who have an interest in Irish and European issues. The main objective of this investigation is to outline the extent of unlicensed peat extraction across the country (historic and present) and key to the investigation is using satellite imagery to visualise/map the changes to peatlands over a series of time.

Unfortunately, the quality of publicly available satellite imagery for the land parcels in Ireland that I am examining is very poor and not up to publication standards.

In this light, I would like to ask if it would be possible to discuss our project with your team with a view to accessing the required satellite imagery to help tell this important story.

With glacier retreat and climate change, high mountain geohazards, including catastrophic landslides and glacial outburst floods, are becoming more common in many mountain ranges around the world. While some of these occur in remote valleys with little downstream impacts, others can be disastrous if they intersect human settlements or infrastructure such as roads or pipelines. For example, in February 2021, a large rock and ice avalanche descended the Ronti Gad and Rishiganga valleys in Uttarakhand, India, destroying two power plants and leaving 200 people dead or missing. I led a large international effort of geoscientists, hazards professionals, remote sensing experts, and social scientists, to understand the geophysical causes of the disaster, and we relied heavily on rapidacquisition, very high-resolution stereo imagery from WorldView, Pleiades, and SkySat to quantify the geomorphic changes caused by the landslide and ensuing debris flow and flood, and to serve as a terrain model on which we ran numerical simulations of the runout. Our first paper (Shugar et al., 2021) was published in Science about five months after the disaster. Similarly, high-resolution topographic data, generated by satellite DEMs or lidar, has been instrumental in understanding other high mountain geohazards. I am involved in other geohazards projects where we are making use of very high-resolution satellite imagery and lidar to quantify massive geomorphic changes. For example, I am building high-resolution DEMs from stereo SkySat and historic air photos to quantify any precursory motion for a landslide onto Canoe Glacier in coastal British Columbia, Canada. The landslide did not kill anyone or destroy infrastructure, but is considered a near-miss, as there is an air strip and access road for a nearby mine, downstream. Like with the Uttarakhand disaster, we are using the DEMs as an input for numerical modeling to determine what might have happened if the landslide was 10% larger, or occurred slightly farther down the valley. In other words, very high-resolution, rapidly acquired satellite data has become a critical part of modern geohazards work. So this proposal requests the ability to acquire high-resolution stereo imagery of geohazards as they develop, and so I cannot provide a specific location of interest. Each disaster is unique, as is the response of governments and international agencies to the event. In some cases, the international scientific community is called on to deliver timely assessments of the ongoing risks, as was the case following Nepal’s Gorkha earthquake, where I was part of a large team delivering evaluations of landslide risk from satellite imagery to agencies including NASA, and the Nepali Army. Aside from reports to organizations such as these, results from the proposed satellite analysis will be as peer-reviewed journal articles.

The Rapid Action on coronavirus and EO dashboard is a platform that demonstrates how the use of Earth observation data can help shed new light on societal and economic changes currently taking place owing to the coronavirus pandemic. Across all European countries and ESA Member States, the dashboard showcases examples of how different analyses over a wide range of Earth observation data coming from the Copernicus Sentinels and Third Party Missions, as well as groundbased observations and advanced numerical models via the Copernicus Services can illustrate these socio-economic and environmental changes. The dashboard not only captures the effects of the lockdown, but also shows how Europe is beginning its recovery and is relaunching a number of activities. In this framework PLES is in charge of keeping coordinate and consolidate the data indicators of the Dashboard and in this activity the Truck Detection indicator need to be updated to allow a more complete and explanantory effect of the pandemic impact on the commercial activities thought the monitoring of trucks traffic and made avaialbe on the dashboard the most recent and complete information regardig this indicator.

The project’s primary objective is to develop and implement a prototype for an EO-based rangeland monitoring system at a continental scale for Africa. It will be based on the synergetic utilization of Sentinel-1 SAR, Sentinel-2, and Sentinel-3 multi-spectral data and shall cover the entire continent at 10m spatial resolution. This EO-based rangeland monitoring system aims to implement tailored algorithms within an end-to-end analysis and production workflow (ranging from data access and analysis to product generation, validation, and dissemination) that facilitates the routine (i.e., annual/seasonal/monthly), synoptic (i.e., a continental scale for Africa) and high-resolution (i.e., 10m) EO-based monitoring of rangeland systems. In addition, this project aims to develop and generate a suite of dedicated higher-level information products such as the spatial and temporal extent of rangeland and relevant land cover types, herbaceous biomass availability, carrying capacity, etc. Furthermore, the rangeland monitoring system is exceptionally user-driven, with priority given to direct response to the requirements and information needs of the key users. It builds upon and exploits the current observational capacity offered by the Copernicus Sentinel missions and will develop innovative and scientifically accurate products directly relevant to rangeland monitoring. The products will enable further application and services to build on top of them that can be utilized by a broad range of actors who expresses an interest in rangelands, from continental to local scales. The project further focuses on capacity development and knowledge transfer to organizations and actors in Africa to enable the operational production of the developed products after the completion of the project activity.

The main objective of this study is the development of a suite of retrieval methodologies to derive ocean geophysical parameters from Sentinel-1 (S-1) SAR data, more specifically Wave-related parameters (Sea State) retrieval from S-1 Interferometric Wide Swath (IWS) TOPS observations.

For a sustainable dam management, information such as lake levels are essential to understand the impact of climate change and extreme weather. Lake level is a key hydrological parameter, which is sensitive to both regional and climate variations, human disturbances and lake bathymetry changes. Water-level changes in lakes were traditionally derived from gauge data. While gauging stations can provide accurate water-level observations, only limited gauged water-level measurements are available in remote areas, such as in Temenggor Lake, Chenderoh and Kenyir lakes in Malaysia. Temenggor lake; located at Royal Belum Forest, is the second largest dam in Malaysia supplying water for domestic use since 1999. Under the tropical climate conditions, the reservoir area is affected by the seasonal climate condition resulting in declining dam water level during hot season, and flooding during wet season. The technology of satellite altimetry has been widely used for monitoring inland waters for more than 30 years, however, such study has yet to be conducted in Malaysia due to its’ relatively small size of lakes/rivers. Taking advantage to the recent and advanced technology of Synthetic Aperture Radar (SAR) altimetry, the proposed project will exploit the high resolution and accurate lake level data from the Sentinel-3A satellite for forecasting the lake levels, which can help to evaluate the impact of climate change on regional water resources. This project is related to the United Nation Sustainable Development Goals (SDGs) which are goal 6: Clean Water and Sanitation, goal 11: Sustainable Cities and Communities, and goal 14: Life below Water. Objectives of Project:

To estimate the rate of water-level changes using the advanced technology of Synthetic Aperture Radar (SAR) altimetry from 2016 to 2022.

To develop a forecasting model for predicting the lake level variations using a deep learning technique.

SentinelHub has been a central piece of our work as it allows us to gather a great amount of information about our most valuable study subject: the Earth. So far, our case studies have included the observation the raise of ocean water levels, the deforestation around the South-West Amazon forest for the development of agriculture and many others.

The use case can contribute to better characterizing the current deformation field of the Gorzano fault, which is part of the complex extensional fault system of the Italian Apennines. The central Apennines is one of the most seismically active areas in Italy. The seismic sequences that recently affected the central Apennines (2009, L’Aquila, and 2016 Umbria-Marche) have shown the surface effects of the extensional strain and confirmed the high level of seismic risk in the area. In particular, the 2009, Mw 6.3, L’Aquila earthquake was caused by the reactivation of the NW-SE trending Paganica normal fault, while the 2016 Central Italy activity, started with a Mw 6.0 Accumoli event and climaxed with the 6.5 Norcia mainshock, was mainly generated on the Vettore – Bove fault but also activated the northern section of the Gorzano Faults. The Gorzano fault is a 27 km-long extensional structure located in between the Vettore-Bove and Paganica faults. On the ground of its dimensions and the paleo-seismological data, it has the potential to release earthquakes up to Mw 6.7- 6.8. It is interpreted as the source of numerous recent moderate magnitude events (09 April 2006, Mw 5.2; 24 August 2016 Mw 6.0; 17 January 2017 Mw 5.3, Mw 5.5, Mw 5.3, and Mw 5.1) whose cumulate seismic moments are estimated to have lowered by only a few tenths degree of magnitude the seismogenic potential of the fault. For this reason, the complete reactivation Gorzano Fault is one of

the possible scenarios in the near future. Surface displacement measurements obtained using DinSAR technology can help to investigate the current state of the fault since they offer an adequate spatial and temporal sampling of the area. In fact, clusters of measures of surface displacements in the order of cm in a year along the fault, in particular at the known extremes of the fault (tip points) might manifest indirect evidence for deep activities relevant from a scientific point of view, but also to require continuous monitoring and attention from Civil Protection defense.

Objectives of the project is to develop services based on Sen2Like Analysis Ready Data products. The Sen2Like s/w is a processing s/w considered as pre operational processor. The provision of data cube in a timely manner and access to large dataset are issues preventing full developpment/promotion of land mapping services in particular multi temporal change detection analysis.

In first, we would like to get access to the data cube for experiment. In addition, post analysis of spatio temporal datastack (subtle change) would be a value added.

The project is focused on deploy geographic and geoespatial data about the Magallenian area (Argentina and Chilean). We will build a web server for cartographic data and we need satellites data to complement and prepare ourdata frames. Mainly for climatic, environment and public and natural hazards.

Our services (WMS, WFS, WMTS, WCS) will be avaliable in our area, for educational center, enterprises or any organization or people that require it. The information required are about cities (Punta Arenas, Porvenir, Puerto Natales) and surrounded areas with emblematic landmarks (Torres del Paine Park, Tarn Mount, Magellan Strait, Ice Camps). Objectives are:

– Deploy geographic and geospatial information about Magallenian area (Argentina and Chilean)

– Create a source of service (WMS, WFS, WMTS, WCS) that can be used for educational center, enterprises or any organization.

– Contribute to grow the cartographic knowledge about Magallenian region and his landmarks, some of that, are world recognized. For example “Torres del Paine”

One of the fundamental objectives of the project is to advance on the description and knowledge of cartography of the patagonic area; to help different levels of teaching, whether students or teachers, to be interested in cartography and to aimed in the creation of cartographic products that can be evaluated, improved and at the same time, shared by the population and other actors.

The ESA InCubed funded project SIAMaaS (SIAM as a Service) aims to automatically transforms Sentinel-2 imagery into transferable, actionable spectral categories in near real time and enables anyone to obtain application independent information layers globally without expert knowledge/skills. Existing approaches to automatically convert Sentinel-2 data into reliable information have limitations (e.g. area-, application-specific, require samples).

Satellite Image Automated Mapper (SIAM)-as-a-Service semantically enriches multi-spectral imagery using a validated, automated, transferable knowledgebased decision tree. The envisaged Web-based service will provide stable, spectral categories for Earth observation imagery analysis and applications – on request for every Sentinel-2 image worldwide.

The research is aimed at mapping of small-scale crop farming in Kenya, with the goal of providing farmers and policy makers with information on cropland area, crop type and crop status. A prototype mobile application will be developed to allow stakeholders to quickly view information pertaining to their area of interest.

The project aims to teach bachelor students the usage and benefits of satellite data in agriculture. The students will be able to request data from predefined fields over many seasons via the sentinelhub python API, calculate different vegetation indices, and do simple statistical analysis with python. The main goal is that future agriculturists and engineers can work with the original sentinelhub platform. In perspective, students will work for well-known companies like producers of agricultural machinery or software companies. Their experience in the python API for sentinel products will increase the acceptance of these companies to work with those data, generating many potential customers for the sentinelhub API. Also, the machine learning part of the course will help to prove that the sentinelhub API is superior to other detailed programs.

The project focuses on creating application maps from predefined fields from the Invekos program. The students will be taught how to elaborate NDVI, true color, and Leaf Area Index requests. Therefore they will be able to apply their knowledge to various other bands and indices. The students will also get a small overview of the positioning of the Sentinel satellites via the delivered angles and get a project to work on from home to increase their skills with the sentinelhub API.

“Snow Coverage Map (SCM), Snow Pack Depth (SPD) and Snow Water Equivalent (SWE) are essential geometric and microphysical properties of snow accumulated during the winter seasons of the terrestrial planet. These parameters are used for various applications, for example, in hydrological modeling for snow melt flow simulations, in civil protection for avalanche warning in mountainous areas, in water resource management to estimate the capacity of groundwater and in cryospheric monitoring to evaluate the seasonal mass balance of glaciers. However, an accurate largescale and high spatial resolution estimation of the snow parameters SCM, SPD and SWE is still an open problem due to the significant influence of the hydrometeorological conditions present in the area of interest and the impossibility of carrying out in situ measurements. The aims of the proposed research project are:

1. The development of specific inversion methods to estimate various snowpack properties (namely SCM, SPD and SWE) from DInSAR and BackSAR data;

2. The implementation of a Sentinel-1 DInSAR processing chain to estimate, on the Gran Sasso area, in Italian Central Apennines, SCM, SPD and SWE for dry and wet snow, combined with SAR backscattered data for wet snow discrimination. Cross-polarized SAR data, together with optical and infrared data from other Earth observation (EO) missions, will also be used for the purpose of identifying snow covered areas. Some external auxiliary data will be used to improve the estimation capability, including a snow mantle dynamical model that helps in training the inversion algorithms and reducing the impact of DInSAR coherence loss on the retrieval accuracy.

3. The implementation of a processing chain that is able to evaluate the seasonal mass balance of the Calderone glacieret situated in the Gran Sasso mountain area, at a resolution around 3 m.

Public entities for the water management and distribution, and the Italian emergency management agency can be considered the main stakeholders that will take advantage of the results.”

Objectives of the projects include the Soil Moisture content prediction (Machine Learning approach) using the NDVI calculated with Sentinel-2 data with the purpose of estimating wildfires risk using soil moisture content prediction.

We have been using SentinelHub as part of our project for a few months now, extracting data from space and earth observation satellites and putting them to use in the benefit of ‘business for good’. In addition to the data extraction, we’re building an education platform to get more people into space and data science, and to guide the younger generations that wish to take on our career path. So far, SentinelHub has helped us create concrete examples of data extraction in earth observation, which has interested more people and has gotten our project more attention. Our platform, SpaceLearn uses examples we’ve extracted ourselves from observation satellites such as Sentinel, Kepler or Copernicus and applies them to interactive lessons about astrophysics, meteorology, ecological concerns and invites them to take part in our participativeresearch project, where all people can help us detect exoplanets.

The project aims to use cloud services to organise the Space4Eergy Hackathon 20222. Building on its experience and expertise in the organisation of Space Hackathons (Copernicus Hackathon Graz 2020, GALACTICA Hackathons 2021), Science Park Graz (SPG) / ESA Space Solutions Austria hereby proposes to partner with BMK and the Green Energy Lab (GEL) to organise the Space4Energy Hackathon 2022.

The Space4Energy Hackathon is an event for start-ups, SMEs, students and young professionals bridging the topics of Space and Energy. Participants from Austria and EU member states are invited to participate, and industry and businesses are invited to bring their teams. Science Park Graz / ESA Space Solutions Austria and Green Energy Lab are organising the Space4Energy Hackathon in October 2022 as cooperation partners of the Austrian Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK). The Hackathon aims to provide solutions based on satellite data and services to answer specific challenges defined by industry players from Austria.

What is it about? How could our increasingly integrated, sustainable energy systems benefit from the abundance of satellite data and services? Which satellite data applications for the green energy future are conceivable, and how are they designed? Bold and innovative ideas are called for here!

Challenges:

• SPACE4BIOMASS BY AUSTRIAN FEDERAL FORESTS Windthrow events in forests – space-based impact assessment;

• SPACE4WIND BY ENERGIE STEIERMARK & RHEOLOGIC Wind turbines;

• SPACE4SPATIALPLANNING BY PROJECT PARTNERS OF “SPATIAL ENERGY PLANNING FOR ENERGY TRANSITION” Spatial energy planning for regions to support the energy transition;

• SPACE4THERMAL BY KELAG & HAKOM Using Localised High-Resolution Land Surface Temperature Products for Thermal Monitoring and Exploration.

The study uses SAR data to develop a new method to improve the depth measurement accuracy of small lakes in the Tibet Plateau. For the analysis of the re-tracking algorithm, the surface height provided in the SARin data Level-2 product of Cryosat-2 is obtained by fitting the Wingham/Wallis model to the waveform data in L1b. However, if there are multiple distinguishable peaks in the echo waveform, this method is prone to false tracking so that the wrong water level can be calculated. In recent years, some scholars have proposed new and Improved algorithms. Among them, when the L1b waveform is not classified, the MwaPP algorithm has the best effect; otherwise, SAMOSA3 is the best. NPPTR and Envisat ICE-1 performed better, but a new robust algorithm was incorporated into this study. For the research on the water level anomaly removal method, Nielsen et al. and Jiang et al. used the technique based on the mixture distribution of Gaussian distribution and Cauchy distribution to calculate the average water level along the track, which significantly reduced the extreme observation value to the mean value.

In addition, in the case of a large number of observations, the static and dynamic models are better; in the case of fewer observations, the dynamic model is more effective; the Ad hoc algorithm tries to eliminate outliers before estimating the water level. Therefore, it is necessary to set subjective criteria for including or excluding each observation; Wang et al. constructed a linear fitting model of the water level along the track and calculated the model water level at the starting point in the latitude direction, taking the mean of the two as the mean along the track. Shen et al. used the 1-time standard deviation comparison method to remove the abnormal water level along the track; Wen Huang et al. calculated the difference between the independent observations and the mean and eliminated outliers by comparing them with the corresponding 3-time standard deviation. The average value is calculated until the conditions are met.

Compared with the traditional pulse finite altimeter, Cryosat-2 has a higher along-track resolution (300m) in the SARin working mode and a very different waveform shape. Its peak trailing edge has a faster descent rate. The polluted echo waveform has multiple identifiable peaks, and the new Baseline-C version data waveform increases the number of sampling gates from 512 to 1024, which makes the effectiveness of the previous re-tracking method suffer. Therefore, improving the existing re-tracking method can ensure a more reliable water level. In addition, detecting and removing abnormal water levels have always been important research content in this field. There are inevitably various errors in the time series, and the traditional methods are most suitable for the situation with many observations. Under the influence of few observations and water level differences, it is difficult to identify and remove gross and residual errors. Furthermore, Interferometry is affected by more factors, such as the distance between the partial observation point and the sub-satellite point. Therefore, improving the ability to detect and remove abnormal water levels is also of great significance to improving the accuracy of the inversion.

In two contrasting sites in terms of urbanization and interaction with their natural resources, changes have been detected that have affected the quality of life of local inhabitants. In this study we intend to carry out a diagnosis of the trajectories of changes from properties adjacent to inhabited areas and to see their impact on human well-being. Characterizing and ranking impoverished services and listing the consensual responses of the population to solve environmental problems. Likewise, projects will be designed and implemented that will be monitored in situ and through satellite sensors techniques. It will be analyzed how both populations face and resolve their environmental problems. A study site is located in Xalapa, the capital of the state of Veracruz, where a drastic change in land use was carried out in the middle of the residential area, aimed at establishing a shopping center, which, due to not meeting the transformation requirements, was canceled, but the neighbor detect microenvironmental and visual changes, to which they will undertake mitigation actions. The other site is completely rural with low density and economic income in the north of the state and it is land transformed from tropical forest to livestock use with profound soil erosion. We will to Implement ecologically and economically viable projects with social relevance that help increase social well-being at the local, regional and state levels., Support to local society that detects local environmental problems in actions aimed at improving their quality of life and environmental awareness Enrich areas in natural recovery with native species with the potential to function as reservoirs of cultural diversity

Our team have previously used the G-POD SARvatore service to process CryoSat-2 observations over the Arctic region during summer months (May-Sep). These have been used to generate the first pan-Arctic summer sea ice freeboard data product for 2011-2020, as part of completed and ongoing ESA/NERC (UK) projects. We would like to now apply the same method to Sentinel-3A&B observations covering the Arctic sector to enable improved summer freeboard coverage and resolution. We request SARvatore for Sentinel-3A&B data processed in EarthConsole PPro for the period 01/05/2019 – 30/09/2019. The altimetry user community (and beyond) to be very interested in our new derived summer sea ice freeboard/thickness products, will benefit of the project results. The results will be available through the British Antarctic Survey Public Data Storage Facility, as for example https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01257

“Teaching remote sensing graduate course for Earth observation. The Master’s of Geoinformation of the Cyprus University of Technology offers a graduate program focused on Earth observation, geo-information and geographical information systems to graduate students who want to expand their knowledge and career prospects in Earth observation. The access to Sentinel and cloud DIAS services will provide a advanced knowledge and perspective on how to work with Earth observation using Copernicus data on a cloud environment. The application will be integrated within the course GEO 553, Remote sensing and Earth observation, GEO552, Geoinformation Data Analysis and GEO551, Geoinformation and GIS to demonstrate the capabilities of Sentinel Hub. The courses will include the ability to understand Copernicus data and services, including what they are, and how they can be accessed and used and understanding how existing Copernicus-enabled services and applications have been developed and deployed. Students will also acquire the skills and knowledge to develop and deploy Copernicus-enabled products and services and to navigate the Copernicus ecosystem. The Sentinel Hub will provide the capability to the students to access service-oriented satellite imagery infrastructure that takes care of all the complexity of handling satellite imagery archive and makes it available for end-users via easy-to-integrate web services. The following features of the system will be demonstrated:

-Full resolution preview over the web

-Time-lapse functionality

-Time-series statistical info service

-Analysis tools for an area or a point of choice

-Script-based on-the-fly definition of new products

-Reprojected WMS services for integration into 3rd party tools

-APIs for advanced feature integration”

TerraZo intends to be an application that allows farmers to analyze their fields with ESA satellite images and generate application maps for fertilizer or other agricultural applications. The service is already operational at terrazo.josephinum.at and is taking advantage of the scihub API from ESA directly. We are downloading all the available data in the region of Austria within the last three years and saving them inside our system. However, downloading all the images can generate issues for future project developments, and we need to overcome the limitations of this approach. For this reason, we are looking for cloud-based alternatives that provide the intended services and satisfy the requirement that they can be provided cost-efficient from the cloud. We are using Docker and plan to bring our service to the cloud (i.e., AWS, GoogleCloud, or Azure). To decide what cloud platform or satellite image provider to use, we want to evaluate these providers first. We are also considering Planetary Computer for the project, which looks pretty cost-efficient but might be unstable in operation. We are also aware of the AWS S3 storage of Sinergise that provides all the data at AWS.

Croatian organizations are preparing to start mapping marine habitats for the first time using EO and acoustic data. I would like to play (test) the usage of multiple EO data together with acoustic multibeam data, side scan sonar data and in preparing optimal spatial sampling and later detecting several marine and habitats, especially spatial distribution of Posidonia oceanica. Hopefully, well mapped Posidonia will be used for better planning of future marine Natura 2000 sites in Croatian part of Adriatic as well research paper will be result of this exercise. Some other habitats of interest can be mapped using EO data due to spatial distribution in water that do not exceed 10 meters together with the coastal habitats.

The main technical objectives of Atlantic cities: Smart, Sustainable and Secure Ports and Protecting the Ocean (ARIA3) are the development, delivery to the end-user community and respective impact assessment of an agreed number of customised Earth Observation-based information services to support decision making processes by local stakeholders in the Atlantic Region. Those services shall cover 9 pilots grouped into three sub-topics: Climate Resilience Services: Impact on Economic Activities Coastal Flooding Risk Assessment Coastal Erosion Risk Assessment Atlantic Cities and Ports Services: Protection of Coastal Assets Security of Ports and Maritime Transport Tourism and Public Health Ports Pollution Monitoring Protecting the Ocean Services: Detection and Monitoring of Marine Litter Good Environmental Status of Marine Areas.

The main objective of the project is to understand how and with what intensity the organization of the geographic space of the territories of the municipalities located in the southeastern region of the state of Mato Grosso, may be exerting pressure on ecological processes of distribution and mobility of wild native species of flora. and fauna of the Cerrado and Pantanal biomes, in the region of influence of the Ecological Corridor area of the São Lourenço-MT river basin. To this end, its team includes researchers from several Brazilian and international universities (University of Reading-UK, Poznan University–PL, Ben-Gurion University of the Negev–IL, Universidad de Buenos Aires-AG, etc.) theme addressed by the research.

The project also aims to involve the student community of public and private schools in the cities of the study area and, as far as possible, the public managers responsible for territorial planning and environmental management of these cities, in part of their activities. This is an innovative strategy that seeks to expand the social function of university research, on the one hand by encouraging scientific initiation in elementary and high school students through their involvement in various stages of research, and on the other, by contributing to public policies are implemented more efficiently, discussing with public managers methodologies and techniques to implement territorial planning and biodiversity conservation actions in urban and rural landscapes in this region. In this way, it is expected that the research will be able to achieve both objectives aimed more specifically at the advancement of scientific knowledge of a geographical, ecological and environmental nature in this region, as well as objectives more aimed at strengthening and expanding the social function of the public university in Brazil and governance of the population in the municipalities where they live.

Deep neural networks achieve very good results in computer vision because they have many parameters and can address very complex pattern recognition problems, as a result, they also need a large amount of labeled data to learn. Well-labeled data can be time consuming and expensive to collect. Marking of buildings and other structures can be difficult in certain domains due to complex or blurred edges, domains that are not ordinary for people are also a problem. This means also experts who label images, can include errors in the labels, which can then affect learning. Deep neural networks are somewhat robust to noise and label errors, as they have to generalize by averaging the error function across batches due to the way they learn. There is even more averaging in semantic segmentation, where in case we have a certain amount of labeled cells, those who are correctly labeled predominate, when calculating the error function, this error is calculated at each pixel and then averaged. Meaning that learning deep neural networks for detection are robust to any amount of noise in labels, if only there are enough labels. Testing the robustness of semantic segmentation showed that labels have a major impact on robustness as it impairs leaning performance, if the error in labels is not addressed. Since we want to achieve the greatest possible robustness, it makes sense to include uncertainty in the labels and take advantage of it while learning. Uncertainty can be modeled in the design of labels itself, thus explicitly or implicitly when modeled during learning. In the master’s thesis, we will discuss the problem of uncertainty in labels in two domains of semantic segmentation where labeling is difficult, either because of the data which contains unusual scenes for humans, such as multi-spectral surface data and microscopic images. We assume that such a way of dealing with labels would contribute to better results of the model and less cognitive effort of markers.

Lebanon is suffering recently from a major economic crisis. Food safety has become a major challenge for our government and society. In the following project, we intend to:

1. study the time series of vegetation areas in Lebanon since the Year 2015.

2.assess the impact of COVID-19 on agricultural areas

3.assess the impact of recent economic crisis on farmlands

4. design and implement a deep learning model to detect crop areas (using pytorch library)

For the montioring of the mass loss in Greenland Ice Sheets, several problems need to be overcome, as the one caused by the sparse crossover density. For that, a repeat-track approach should be used when using radar altimeter data for determining mass trends. In addition, the situations in the margins always show difficult because its complex terrain and altimeter data does not work well, and over ice surfaces, the altimeter waveforms are complicated by variations in the temperature in the snow pack/firn layer, indicating that proposed a new waveform retracking method is really meaningful. Traditionally, the altimetry-based estimates of the mass balance are often presented as relatively long-term averages (Shepherd et al. 2020, Yang et al., 2018, Hurkmans et al., 2014). But if we can increase the spatial and temporal resolution of the time series of the mass balance, we can see some melting and winter accumulation parts, which could be useful for the independent test of climate models. So, exploring the time-evolving mass balance of the GrIS in seasonal scale or even month scale is really meaningful to describe the relationship between the global climate change and the sea level rise.

Given the coarse hydrometric monitoring network, it is challenging to efficiently monitoring surface water dynamics and to effectively deal with droughts and floods. These extreme events are expected to increase in frequency and magnitude under climate change as well as urbanization. The advantages of multi-altimetry measurements are the global coverage and the longtime span, facilitating the research for the estimation of the river discharges with optimal space and time resolution. Moreover, sophisticated processing techniques of data acquired by the upcoming SWOT (Surface Water and Ocean Topography) allow the retrieval of ultra-high resolution water level profiles. The mission SWOT will provide critical information on the spatial variability of water surface elevation and allow a better understanding of the interactions between hydrodynamic processes.

Combining data from several altimetry missions, including SWOT, to characterize river discharge over the entire basin is essential for many important applications, such as flood forecasting, water resources management, engineering design, and reservoir operation among others. -Who will benefit from the project results: Jiaming Chen, Astronomical Physical and Mathematical Geodesy Group, Chinese Academy of Science -Results format: public papers, available code

The objective is to apply time series analysis methods to Landsat 5 and Landsat 7 imagery between 1990 and present-day in order to study how vegetation cover in the study areas changed. This time interval chosen includes the years in which the areas where transformed to nature reserves (early 1990s), the year in which the fences between the reserves and Kruger National Park were closed down (1993) and the years in which elephant populations increased drastically (recent decades). Vegetation cover is estimated by calculating vegetation indices. Changes in vegetation cover are linked to the growth of elephant populations in the reserves and to controlling variables (precipitation, bush fires, water availability). The study areas are five private nature reserves in Greater Kruger Area, South Africa. Until recently, the state of vegetation in the study areas is monitored exclusively by field surveys. The research is commissioned by the Agricultural Research Council (https://www.arc.agric.za/Pages/Home.aspx).

The project, part of a MSc thesis, aimes to explore the potential of remote sensing data in monitoring vegetation changes, since this is much cheaper and efficient than field surveys. The Agricultural Research Council will use the results to improve their management practices, in order to secure the biodiversity of the reserves.

According to the EU Ship Recycling Regulation, from 31 December 2018, large commercial seagoing vessels flying the flag of an EU Member State may be recycled only in safe and sound ship recycling facilities included in the European List of ship recycling facilities (“European List”). On this basis, the project aims to assess the potential of development for innovative applications meeting the needs and requirements of the maritime law enforcement, legal compliance and security communities for ship recycling monitoring and enforcement. Additional technical development may be tested according to the needs, requirements and priorities expressed by the end-user community in the maritime sector during a series of consultations (e.g. monitoring of ship recycling plans, of certificates issued or of requirements necessary for ship recycling facilities to be included in the European List). The planned activities aim to elaborate and validate/invalidate a first series of Sat EO based investigation and intelligence services, applications and products to monitor the respect of the EU Ship Recycling Regulation. The technical results include the construction of new EO datasets and processes as well as data fusion capabilities combining EO and nonEO data (e.g. AIS data). They will further involve tests related to the integration of the EO process into a web platform to provide easy access to the Sat EO services developed.

Scenarios to be assessed in relation to the study : Detection of EU ships heading to or located in a ship recycling facility not included in the European list of ship recycling facilities in violation of EU laws (ship recycling facility where the ship is to be recycled according to recycling plan and survey). Verification of EU ships plans to head toward and be dismantled into a ship recycling facility included in the European list of ship recycling facilities. Monitoring of listed recycling facilities to assess compliance with ship recycling plan (e.g. absence of illegal spills or other hazardous waste leaks around the recycling infrastructure)

The Brazilian National Institute for Space Research (INPE) has been producing yearly estimates and detailed maps of deforestation in the Brazilian Amazonia tropical forest since 1988. These maps are extremely important, given the relevance of the Amazon forest in terms of biodiversity, climate and GHG emissions. Currently, the deforestation maps are produced by visual interpretation of Landsat images. This process guarantees a high-quality product, but has a high cost in terms of the human resources involved. The proposed project will investigate the use of Sentinel-2 ARD data cubes in connection with state-of-the-art machine learning methods to produce detailed Amazon deforestation maps. The proposed method is to use algorithms for satellite image time series analysis, available in the R package “sits” (https://github.com/e-sensing/sits). The requested sponsorship from NOR will cover a one-year Basic subscription to the Sentinel Hub, that would allow remote access via web services. The project will be an important showcase for using Sentinel-2 data. It will cover an huge area of 4 million km2 (larger than Europe). It will provide a unique product of global importance.

This project is the prolongation of the project for UbiSAP.

The UbiSAP project developed a digital platform for enabling:

• Acquisition and integration of different data streams with ESA data repositories, including IοΤ and ESA data sources (from crowdsourced data initiatives, ΕΟ data, NAV-SCI data, etc.);

• Visualization and analysis of the acquired data;

• Future integration of data and processing assets (e.g. new data sources and/or new data processing applications.

The projects mandate the development and submission of two different Software Deliverables:

1. Platform – corresponding to the actual UbiSAP platform that enables the objectives listed above;

2. Use Cases – corresponding to twο demonstrators in the NAV-SCI and ΕΟ domain to validate the platform’s functionalities. The UbiSAP project has been dimensioned to last 18 months with extra nine months of the warranty period during which the project partners are operating and maintaining the infrastructure and services for ESA to be able to validate and use the developed platform.

The main project objectives are the study, the extension and the application Prediction (CP) techniques for functional time series with bivariate domain, the derivation of prediction bands and the quantification of the predictive efficiency. However the work is at early stage, thus more objectives may arise during its course.

We created a novel windowed large time-series (we call deep-temporal) urban monitoring method using multi-modal remote sensing data to detect urban changes within each window. We have demonstrated that method with ERS-1/2 & Landsat 5 ΤΜ (1991-2011) and Sentinel 1 & 2 (2017-2021). The core is an ensemble of neural networks trained fully automatically using synthetic labels created to form a combination of state-of-the-art methods. In a follow-up publication (currently under review), we further optimize the pre-trained network for a different area of interest with improved detection capabilities and transfer learning. This method included a minimal manual process of creating ground truths. So far, we have used Google Earth historical imagery.

We further plan to optimize our novel methods to detect urban changes using Sentinel 1 & 2 data. However, we’d need more very-high-resolution imagery to create ground truths for further transfer learning and validation purposes. The requested access to VHR data would help us have more confidence in changes that happened on the surface, resulting in better quality ground truth, and hence a transferred model that can be better trained and evaluated. Our current approach is promising, but we still see a high error rate due to the approximation using only Google Earth imagery.

This project continues ESA’s BLENDED project in that we’ve been involved in 2020/21.

Here, we wish to utilise this method to constrain ground deformation across the United Kingdom from November 2015 – December 2018 and evaluate the susceptibility of shrink-swell related damage at national scale. Subsequently, the results of PSBAS, i.e., the rates of ground deformation, will be used alongside a host of geospatial data related to climatic topographic, lithological, and soil properties to fit a statistical model. The statistical model will be used to project future ground displacement relating shrink-swell process under different climate scenarios across the United Kingdom. The time interval was selected based on the number subsidence related insurance claims which reached a ten year high in 2018. Training the model on a relative extreme is interpreted to give the model the greatest ability to accurately predict under future climate scenarios where due to changes in climate shrink-swell processes are likely to be stronger. We wish to constrain ground displacement in the previous years to relate ground displacement data to long term observed precipitation and temperature data which are primary drivers in the severity of shrink-swell wetting cycles.

In summary, the primary objectives of this process are to:

1. constrain ground displacement rates across the UK in 2016, 2017, 2018 using the PSBAS processing chain

2. evaluate present day susceptibility to shrink-swell related damage to property and assets

Additional, longer-term goals are to:

3. use past ground deformation rates (constrained through PSBAS) alongside climatic, topographic, lithological, and soil property data to fit a statistical model

4. use the trained statistical model to evaluate the primary drivers in ground deformation across the UK

5. use the statistical model to predict future ground deformation rates under different climate scenarios to evaluate how shrink-swell damage will be impacted by climate change.

The results of this study would be the first application of InSAR to measure ground displacement at a national scale and provide essential information on the susceptibility of locations to structural damage. Additionally, the innovative approach to use statistical techniques to project subsidence risk under future

climate scenarios would be the first application integrating PSBAS with statistical modelling across the globe. This evaluation will provide the first quantitative estimates of ground displacement under high and low emission climate scenarios, which will not only improve the accuracy of current predictions (i.e., BGS, Cranfield University predictions) but also provide valuable information for financial loss models. For research and education purposes, we could provide the GEP Portal with the map of ground deformation (i.e., a rasterised map) across the UK as produced through PSBAS, however, the processed time-series data and the results of the statistical model would not be shared on GEP.

The project deals with a pre-commercial exploration together with a startup viehfinder.com from Austria, to localize grazing cows on Alps. With the help of GNSS and LoRaWAN (Long Range Wide Area Network) we localize each cow, store the movement pattern of each cow in a cloud based spatial database. The product shall enable:

-to provide clear evidence which cow has been on which alpine region, over which time period

-analyze the grazing/movement patterns

-the farmer to find cows that are missing

In addition, the project consortium is about to explore the usage of a WebGIS portal to visualize the movement data of the cows and to plan LoRaWAN antenna positions (with the help of spatial optimization methodologies). In order to plan the antenna positions accordingly and to visualize the data properly the WebGIS benefit from a subscription of Sentinel data – in order to provide European-wide remote sensing data on the area of interest.

We intend to perform wide area Interferometric SAR (InSAR) processing based on hosted services available on the Geohazards Exploitation Platform (GEP). Our main goal is to verify the robustness of platform based solution in covering wide areas. We shall propose a methodological approach to reduce error budget included in the InSAR processing when such large processing extends are considered as well as the post-processing efforts required to combine individual results from different satellite tracks. Apart from the above mentioned research objectives, the generated dataset with country wide coverage, in our case entire Greek territory, shall be opened and disseminated to the scientific community via GEP e-collaboration tools for further utilization in geohazards related applications. Finally, such dataset may potentially support inter-verification activities of other InSAR measurements generated on comparable wide spatial scales (e.g. EGMS products).

This proposal is a component of the ESA contract GEP (Contract No.: 4000115208/15/I-NB) concerning the pre-operational demonstration activity. This ESA driven initiative consists of a wide area mapping pilot using the new service chain SNAPPING based on the ESA SNAP toolbox. The pilot it has the purpose to deliver PSI points over a large area and measure the ability of the platform to support mass production using the built-in operational metrics on the GEP. AUTh will deliver a technical report with technical feedback of the pilot and a scientific assessment of the impact and benefit of the service delivery over this high seismic risk region.

In this project, it is proposed to develop wildfire fuel map using hyperspectral imagery of PRISMA, a fundamental satellite of Italian Space Agency. For which, previously, detailed classification of vegetation types is required. In order to classify different vegetation types using various machine learning classifiers including quantum classifiers, there’s a requirement of virtual machine for processing.

-Who will benefit from the project results: Wildfire fuel map is useful for various purposes in fire risk modelling, post fire events, to develop vulnerability map etc., which would be useful to researchers, firefighters and also to stakeholders. -Results format: Result will be made available in GTiff image format and with no specific conditions. -Area of Study: Italy

The overarching goal of the WorldWater project is to empower national and regional stakeholders with advanced Earth Observation (EO) data and tools to better monitor their water resources and report on the global water agenda such as the 6th Sustainable Development Goal (SDG) on water and sanitation of the 2030 Agenda on Sustainable Development.

The WorldWater project will develop novel, robust and transferrable EO solutions for monitoring surface water dynamics both in extent and volume, which can be exploited by a large community of stakeholders involved in water management and active at all scales, from local water management to national water strategies, up to transboundary river basin management plans or largescale assessment of surface water changes.

The WorldWater project aims principally at strengthening EO capacities in countries to monitor their inland water bodies (lakes, reservoirs, rivers, and estuaries) and consequently improve their national decision-making processes on water resource management and water security.