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 address a major …
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 …
How does taking into account vertical variability of Soil Moisture impact on predicting vegetation productivity? Does this impact depend on climatic areas and vegetation types? These are the main questions addressed by a recently published paper, outcome of Living Planet Fellow Sophia Walther research. The study demonstrates how, at the global scale, semi-arid areas and …
The AI for the Arctic (AI4ARCTIC) project applies deep learning, in particular deep convolutional neural networks, for Earth observation applications within the cryosphere, focusing on sea ice and snow. The project trains deep-learning systems from relevant training data, and tests and demonstrates the capability of deep learning by applying it to large-scale inference of cryosphere-related …
Matthew has been at Leeds since 2014 and was initially a PhD candidate, before completing his studies and becoming a postdoc in 2019. Since July 2020, he has been a European Space Agency Living Planet Fellow, and is part funded by the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) and …
Living Planet Fellowship research project carried out by Matthew Gaddes. The Earth’s subaerial volcanoes pose a variety of threats to humanity, yet the vast majority remain unmonitored. However, with the advent of the latest synthetic aperture radar (SAR) satellites, interferometric SAR (InSAR) has evolved into a tool that can be used to monitor the majority …
