GeoFMs extend this trajectory through large-scale self-supervised pretraining on Earth observation data, offering the potential to reduce dependence on task-specific labelled datasets and transfer learned representations across contexts and prediction tasks.
This survey develops a unified perspective by organizing probabilistic forecasting methods according to where and how uncertainty is introduced into the forecasting pipeline, and identifies unresolved challenges surrounding uncertainty in evolving dependency structures, physics-informed predictive distributions, foreca...
Donia Besher, Rajdeep Pathak, Madhurima Panja et al.· 0 citations
By combining autonomous multimodal planetary data discovery with targeted model optimization, PPE lowers the technical barrier to planetary-scale analytics, enabling rapid, customized, expert-level deployment.
Ecosystems and the services they provide are essential for life but continue to undergo degradation worldwide. Satellite remote sensing has been essential for environmental mapping for decades, but can suffer poor accuracy when applied to mapping terrestrial ecosystems. Geospatial Foundation Models (GeoFMs) integrate d...
George W. Lucas, Benjamin J. Cresswell, S. Duce et al.· Remote Sensing· 0 citations
This survey reviews the development of Earth embeddings from foundation model pretraining and reusable encoders to global and near-global embedding products, and summarizes the technical and scientific challenges surrounding global embedding products.
These findings identify the practical regime in which pretrained representations are most valuable and suggest that instance discrimination, rather than forest semantics, is the main remaining obstacle to a general-purpose 3D forest foundation model.
Yuan-Wen Yue, Stefano Puliti, Damien Robert et al.· 0 citations
Deep learning hydrological models rely on static catchment attributes to characterize basin heterogeneity, but these traditional descriptors suffer from regional inconsistency and limited temporal representativeness. This study evaluates geospatial foundation model embeddings (from AlphaEarth and StefaLand) as transfer...
Jiangtao Liu, Chaopeng Shen, Yuan Yang et al.· Geophysical Research Letters· 0 citations
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