Satlas

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)

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)

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

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)

Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)

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)