research

2026

1. Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts

   Nils Lehmann, Jakob Gawlikowski, Burak Ekim, and 2 more authors

   arXiv preprint arXiv:2608.16614, Aug 2026

   arXiv
2. GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI

   Naomi Simumba^*, Nils Lehmann^*, Paolo Fraccaro^*, and 9 more authors

   Transactions on Machine Learning Research, Jul 2026

   arXiv HTML PDF
3. No One Knows the State of the Art in Geospatial Foundation Models

   Isaac Corley, Nils Lehmann, Caleb Robinson, and 6 more authors

   arXiv preprint arXiv:2605.12678, Jun 2026

   arXiv
4. EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data

   Nils Lehmann, Yi Wang, Zhitong Xiong, and 1 more author

   In ICLR 2026 Workshop on Machine Learning for Remote Sensing (ML4RS), Main Track, Apr 2026

   arXiv HTML PDF Code

2025

1. Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation

   Leonard Waldmann, Ando Shah, Yi Wang, and 6 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun 2025
2. Lightning UQ Box: Uncertainty Quantification for Neural Networks

   Nils Lehmann, Nina Maria Gottschling, Jakob Gawlikowski, and 3 more authors

   Journal of Machine Learning Research, Jun 2025
3. The Missing Piece: Standardising for AI-ready Earth Observation Datasets

   Cesar Aybar, Julio Contreras, Chen Ma, and 9 more authors

   In TerraBytes - ICML 2025 workshop, Jun 2025

2023

1. GEO-Bench: Toward Foundation Models for Earth Monitoring

   Alexandre Lacoste^*, Nils Lehmann^*, Pau Rodriguez, and 14 more authors

   In Advances in Neural Information Processing Systems, Jun 2023
2. SSL4EO-L: Datasets and Foundation Models for Landsat Imagery

   Adam Stewart, Nils Lehmann, Isaac Corley, and 6 more authors

   In Advances in Neural Information Processing Systems, Jun 2023