The TEP Urban platform offers the possibility to define and conduct on-demand processing and production of thematic content based on multi-mission remote sensing imagery (in particular ESA missions including the Sentinel suite and Landsat imagery) and additional data sources (e.g., cadastral data, statistics, socio-economic data). The additional data might either be publicly available on the platform or individually uploaded …
To support future sustainable and efficient farming and aquaculture, the FS-TEP addresses the needs of a heterogeneous user community, spanning from agriculture to aquaculture, from small-scale farmers to agricultural industry, from public science to the finance and insurance sectors, from local and national administration to international agencies. EO data plays a major role in understanding and managing …
C-TEP is a data access service dedicated to improving the efficiently of data-intensive research into our dynamic coastal areas. The C-TEP platform provides advanced tools and data management resources to easily process and manage large volumes of coastal data and make it easily accessible to users. C-TEP is an online service, funded by ESA, which will provide access to over 20 …
The F-TEP serves commercial, governmental and research users in the forestry sector. It enables easy access to satellite data from any global location. The platform offers on-line processing services and tools to generate value-added forest information products. Via the platform, the users can also create and share their own processing services, tools and products. The platform offers pre-processed optical and radar data from …
The amount of data coming from imaging sensors increases steadily and a modern imaging sensor creates frames of several megapixels at a high frame acquisition rate. These imaging sensors with their large data output are mounted on spaceborne platforms, but the downlink capability of these spaceborne platforms, especially for small platforms, has not been increasing …
Traditional empirical and analytical Earth Observation (EO) algorithms retrieving physical parameters are getting to a fundamental change where learning algorithms without any prior background will be able to set themselves through the ingestion of Inputs/Outputs training datasets. Nowadays, Deep Learning (DL) networks among many other Machine Learning (ML) techniques are accurate enough, and computation technology …
Recent years have brought tremendous advancements in the area of automated information extraction from Earth Observation (EO) imagery, but problems still remain since even state-of-the-art algorithms based on imagery alone do not provide a satisfactory solution. In these situations, it is possible to exploit the crowdsourcing of human intelligence, which is a recent promising area …
EO derived information are increasingly being used as the basis for a range of sensitive decisions linked to commercial operations, public safety and environmental security. At the same time, developments in ICT capability enable an expanded volume of information to be generated using distributed approaches such as cloud based storage and processing and platform based …
At present NGOs/CSOs have limited expertise in accessing and utilizing EO data. This project is working with NGOs adressinghuman rights concerns and will develop methodologies for integrating in-situ (citizen data collection), drone and EO data to enhance the collection of information and evidence on activities affecting human rights in developing countries
The project prototyped a prototype of a processing workflow for improved Sentinel-1 backscatter data, providing a temporal stack of analysis ready data (ARD) that can be integrated into a data cube system allowing to access the data in a spatial and temporal domain.
