The project aims at the development of algorithms for cross-calibration of hyperscout with S-2 and the development of the phi-sat 1 brain for cloud detection. The team is assessing Machine Learning methodology, techniques, and a suite of applications (ranging from Radiometric calibration of a single band L2A data, Calibration of data products, e.g. NDVI or …
The main objectives of the activity are: to develop and evaluate an approach to performing effective data fusion of multispectral data on-board satellites (SWIR imager of TRISAT mission). The focus of the activity was on identifying (in collaboration with downstream users) Regions-of-Interest, then developing image processing techniques (band selection, compression, fingerprinting and classification) that can …
An unprecedented richness of data is captured by satellites every day, resulting in an ever-growing time series of EO data. Despite the extensive availability of data, there are still many challenges when defining procedures for extracting relevant information from long time series. The goal of the AI4FOOD project is to tackle the challenges that arise …
One year ago, on 20 October 2021, the new baseline global land cover product at 10 m resolution, exploiting Sentinel-1 and Sentinel-2 data, showing 11 land cover classes with unprecedented detail, was launched. Last 28 October 2022, 9 months from the last data take, the global product with data from year 2021 was released. Read …
Atmospheric aerosol is one of the main drivers of climate changes. Importance of accurate global aerosol characterization for climate studies and air pollution monitoring is a well recognized problem (e.g., see IPCC AR5 by Boucher et al.2013). In addition to the traditional spectral Aerosol Optical Depth (AOD) such characterization should also include such extended aerosol …
The main objective of the RepreSent project is to capitalize on the potential of artificial intelligence (AI) and Earth observation (EO) by exploiting the non-supervised learning paradigms. In this context, it is essential to come up with non-supervised learning-based solutions for impactful use cases that use unlabeled EO data. The project started on April 1st, …
QueryPlanet aims at democratizing the access of Artificial Intelligence to the Earth Observation community by developing open-tools for the creation of AI-ready EO dataset and use-cases that leverage such datasets to build global insights applications. Material and datasets develop under QueryPlanet are open-access. The project aims at providing open-source tools for the exploitation of Earth …
The objective of this activity is to develop the most accurate free and open cloud mask for Sentinel-2 with global coverage and to make it available for global Earth Observation community and promote its usage. For this it is required to separate cloud free areas from cloud- and cloud shadow-corrupted areas as accurately as possible …
The project will develop an EO-service for Hyper-resolution crop yield estimates and extreme events crops shocks monitoring by integrating multiple SATellite data and modeling, able to support farmers precision agriculture.This will be based on a dynamic calibration/validation system which takes advantage of the high spatial resolution (5-8m) of combined harvesters’ crop yield information. The system …
Within “ARIES” experimental EO analysis techniques will be developed and validated, addressing water resources management and food security matters. Those techniques,algorithms and prototype solutions will leverage a new generation of upcoming operational EO data: thermal and hyperspectral. In addition to established EO resources, especially Copernicus driven Sentinel-2 and Sentinel-3, the intended ECOSTRESS and PRISMA data …
