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cs.CV2026
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
Amandeep Kaur, Mirali Purohit, Gedeon Muhawenayo +2
New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are la…
cs.CV2024
Classification Drives Geographic Bias in Street Scene Segmentation
Rahul Nair, Gabriel Tseng, Esther Rolf +2
Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image…