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 …
AI4DROUGHT is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster. The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO and …
DEEP EXTREMES is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster. The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO and AI to …
extrAIM (AI-enhanced uncertainty quantification of satellite-derived hydroclimatic extremes) is part of the AI4SCIENCE activity. The first AI4SCIENCE ITT was launched in 2021 and had a focus on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster. The AI4SCIENCE ITT had 2 main objectives: Advancing Earth System Science: advancing our capacity to combine EO …
The proposed IMITATE project aims to address the following questions: How well can machine learning methods emulate physical process-based land surface models, focused over Europe? Can explainable AI techniques provide new insights into process understanding when combining land surface models and Earth Observation data Are the learnt relationships between the modeled inputs and outputs consistent …
