RSC4Earth
RSC4Earth
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Data
AI
ARCEME
Use high resolution remote sensing data and machine learning to develop
A
daptation and
R
esilience to
C
limate
E
xtremes and
M
ulti-hazard
E
vents
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TEE Cube
Time-varying AI-based mapping of ecosystem conditions and extents using multi-source Earth observation data cubes - TEE cube
DeepFeatures
This project aims to create an holistic datacube with latent space variables based on Sentinel-2 spectral indices
DeepExtremes
Develop and test novel artificial intelligence explanatory tools to decipher compound events (e.g., drought and heatwave) based on “mini-cubes” with high spatial resolution.
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