3 citations · 4 across the 5 of their papers we have counts for
5 papers
SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events
Hugo Porta, Emanuele Dalsasso, Chang Xu +2
The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning. However, th…
Physics-Informed Super-Resolution of Atmospheric Data
Chang Xu, Gencer Sumbul, Hugo Porta +3
In the context of global warming, extreme events have become more frequent and intense, making their trustworthy detection and forecasting more important than ever. Yet, atmospheri…
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
Hugo Porta, Emanuele Dalsasso, Jessica L. McCarty +1
Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and emitting large quantities of CO2…
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
Hugo Porta, Emanuele Dalsasso, Diego Marcos +1
Prototypical part learning is emerging as a promising approach for making semantic segmentation interpretable. The model selects real patches seen during training as prototypes and…
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring
Florent Forest, Hugo Porta, Devis Tuia +1
Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas…