Although spectral indices (SIs) represent specific enhanced properties of the Earth’s system dynamics, there is no holistic, data driven approach that combines them. Studies typically focus on a few SIs out of more than two hundred. Our AI approach synthesises and optimises these SIs by using AI dimension reduction to reduce the number variables required …
Extreme weather and climate hazard can cause dramatic impacts on both natural ecosystems and human wellbeing and/or infrastructure and thereby harm society as a whole. Of particular concern are compound weather and climate extreme events, where the hazardous conditions of various extreme events combine and may have more detrimental effects than individual extremes. Increasing the …
The instability of Antarctic ice shelves is one of the most critical open questions in polar science, due to its capacity to drive rapid sea level change at – and beyond – current high-end climate projections. Yet forecasting future instability is notoriously difficult because of the complex, non-linear forcing mechanisms controlling an ice shelf’s response, …
The component of the Earth System addressed in this project is snow cover in mountain regions. The main scientific and technical objectives of AI4Snow are the development and training of AI methods to greatly improve remote sensing-based snow cover products for mountain regions as well as the implementation of data cubes containing all necessary datasets …
Methane (CH4) is a significant anthropogenic greenhouse gas, second only to carbon dioxide (CO2) in contributing to global warming. It constitutes approximately 25% of the warming effect since pre-industrial times. Traditional methods for monitoring methane emissions rely on bottom-up approaches, calculating emissions by multiplying activity levels with emission factors. Satellites offer a more efficient alternative, …
The Deep Earth System Data Lab (DeepESDL) delivers a service to the Earth Science community. The lab provides convenient access to relevant data sets, many of them generated in ESA’s science projects, in an analysis-ready format. Moreover, DeepESDL comprises a powerful data science environment with a focus on machine-learning and artificial intelligence workflows. We are …
The project aims to bring robust, accountable, and scalable multi-spectral super-resolution techniques to the Earth Observation (EO) community for the ubiquitous L2 and L3 pre-processing of the Sentinel-2 (S2) revisits archive. Super-resolution (SR) is a nascent technology and the roadmap to maturity will require insights from many disciplines. Super-resolution is not just about image generation, …
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 …
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 …
