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 …
FDL Europe is a research collaboration between the ESA’s ɸ-Lab, ESA Esrin and Trillium Technologies and the University of Oxford, in partnership with private industry players such as Google Cloud, Nvidia – Scan, D-Orbit and Planet, in which the latest tools and techniques in Artificial Intelligence (AI) and Machine Learning (ML) are applied to research …
The amount of available satellite imagery data has been substantially growing since the start of the Sentinel missions. Nevertheless, applications of AI to EO data are still scarce. The main goal of the AiTLAS project is to facilitate the uptake of EO data by AI experts and vice versa – the uptake of (advanced) AI …
Hyperspectral imaging can capture hundreds of images acquired for narrow and continuous spectral bands across the electromagnetic spectrum, hence can allow us to precisely analyze the materials that are present in a scene of interest. However, the large volume of hyperspectral images (HSIs) makes their manual analysis and transfer very costly and time-expensive, especially when …
EO data allows us to gather massive global information about our planet Earth’s physical, chemical and biological systems via satellites carrying remote sensing devices. When large amounts of data are concerned, such as those captured by remote sensing devices on satellites used in EO, our computers and AI algorithms can be used to help us …
AI4SAR is an attempt to harness AI techniques for high-resolution, high-fidelity SAR data, both in time and spatially. It aims to process rapid-revisit SAR data and develop modular applications for high-frequency monitoring of Earth. The inherent complexity of the backscattered SAR signal presents a daunting challenge to data scientists and machine learning (ML) engineers, thereby increasing …
This collaborative tool on artisanal gold mining (ASM) and security will take the form of a secure web mapping platform. The prototype will include some layers of geographic information and various satellite images of ASM areas resulting from automated detection by artificial intelligence, and will propose spatialized indicators and analytics, for example on the evolution …
The agricultural commodities market is one of the most active, liquid and vibrant markets in the world thanks to its mix of participants, ranging from individuals to farmers, commercial firms, large corporate companies and government institutions, accessing it from anywhere in the world. Soybeans and soybean by-products are the most-traded agricultural commodities, comprising more than …
The main objective of SPATIAL is to provide a proof-of-concept (PoC) prototype for forecasting soybean futures contracts price moves using Artificial Intelligence models based on financial & macroeconomic features and Earth Observation products. SPATIAL is realizing two distinct Machine Learning (ML) models, one for soybean crop yield forecasting and one for prediction of soybeans futures …
