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cs.LG2026
What's in an Earth Embedding? An Explainability Analysis of Location Encoders
Livia Betti, Sebastian Ricke, Ivica Obadic +2
Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural net…
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…