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Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts
Nils Lehmann, Jakob Gawlikowski, Burak Ekim +2
Geospatial Foundation Models (GeoFMs) are most commonly ranked and selected by accuracy on standard benchmark conditions via averaged ranks. We show that this protocol is too narro…
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart +4
Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, de…