5 papers · 1 filter
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
Advancing Earth Observation Through Machine Learning: A TorchGeo Tutorial
Caleb Robinson, Nils Lehmann, Adam J. Stewart +4
Earth observation machine learning pipelines differ fundamentally from standard computer vision workflows. Imagery is typically delivered as large, georeferenced scenes, labels may…
EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data
Nils Lehmann, Yi Wang, Zhitong Xiong +1
State-of-the-art generative image and video models rely heavily on tokenizers that compress high-dimensional inputs into more efficient latent representations. While this paradigm…
GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI
Naomi Simumba, Nils Lehmann, Paolo Fraccaro +9
Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framewor…
DOFA-CLIP: Multimodal Vision-Language Foundation Models for Earth Observation
Zhitong Xiong, Yi Wang, Weikang Yu +7
Earth observation (EO) spans a broad spectrum of modalities, including optical, radar, multispectral, and hyperspectral data, each capturing distinct environmental signals. However…