4 papers
Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing
Mathieu Dario, Florent Chenevier, Kévin Delmas +2
Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with independent methods emerging from…
Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro
Baptiste Clemence, Thomas Hallopeau, Vanderlei Pascoal De Matos +2
Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement charact…
Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sources
Sara Mobsite, Renaud Hostache, Laure Berti Equille +2
Supervised deep learning for land cover semantic segmentation (LCS) relies on labeled satellite data. However, most existing Sentinel-2 datasets are cloud-free, which limits their…
Vision Backbone Efficient Selection for Image Classification in Low-Data Regimes
Joris Guerin, Shray Bansal, Amirreza Shaban +2
Transfer learning has become an essential tool in modern computer vision, allowing practitioners to leverage backbones, pretrained on large datasets, to train successful models fro…