6 papers
Earth Embeddings
Adam J. Stewart, Heng Fang, Isaac A. Corley +1
Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to d…
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…
Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access
Heng Fang, Adam J. Stewart, Isaac Corley +2
Geospatial Foundation Models (GFMs) provide powerful representations, but high compute costs hinder their widespread use. Pre-computed embedding data products offer a practical "fr…
Leveraging Satellite Image Time Series for Accurate Extreme Event Detection
Heng Fang, Hossein Azizpour
Climate change is leading to an increase in extreme weather events, causing significant environmental damage and loss of life. Early detection of such events is essential for impro…
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration
Sebastian Hafner, Heng Fang, Hossein Azizpour +1
Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable devel…
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
Valerio Marsocci, Yuru Jia, Georges Le Bellier +12
Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow.…