EarthShift is a new benchmark using paired datasets to measure robustness of geospatial foundation models to realistic distribution shifts, finding consistent 15-20% performance drops out-of-distribution across 8 models and 11 tasks.
Earthnets: Empowering ai in earth obser- vation
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Transformer backbones with mean pooling and combined self-supervised embeddings yield robust, compact representations for EO tasks that are over 500x smaller than raw data.
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.
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EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
EarthShift is a new benchmark using paired datasets to measure robustness of geospatial foundation models to realistic distribution shifts, finding consistent 15-20% performance drops out-of-distribution across 8 models and 11 tasks.
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How to Embed Matters: Evaluation of EO Embedding Design Choices
Transformer backbones with mean pooling and combined self-supervised embeddings yield robust, compact representations for EO tasks that are over 500x smaller than raw data.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.