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First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)

University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks

PyTorch domain library for geospatial deep learning providing standardized datasets, samplers, transforms, and pre-trained models for remote sensing, land cover mapping, and environmental monitoring (Microsoft, 4K+ stars)

Open-source self-supervised vision foundation model for Earth observation by Clay Foundation (non-profit), a Masked Autoencoder ViT pretrained on multimodal satellite imagery (Sentinel-1/2, Landsat 8-9, NAIP, MODIS, LINZ DEM) with location/time embeddings, supporting classification, segmentation, change detection, similarity search, and few-shot downstream geospatial tasks (Apache 2.0, v1.5 2024-2025)

Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)

Allen Institute for AI's global geospatial foundation model for satellite imagery analysis, enabling large-scale mapping of buildings, wind turbines, trees, and land cover from Sentinel-2 data with open-source weights and inference tools (2024)

Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks

Versatile multi-temporal geospatial foundation model for Earth observation, built on a ViT-based masked autoencoder with 3D spatiotemporal patch embeddings and geolocation/temporal metadata encoding; pretrained on 4.2M global time-series samples from NASA's Harmonized Landsat and Sentinel-2 archive at 30m resolution, with 300M/600M parameter variants and fine-tuning configs for flood detection, wildfire scar, landslide detection, crop segmentation, land cover, and biomass estimation (258+ stars, MIT License)