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AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission
Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing.
CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data
Tropical forests are a key component of the global carbon cycle.
Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data
For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A.
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