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Python package for segmenting geospatial data with the Segment Anything Model (SAM), enabling zero-shot object segmentation in satellite and aerial imagery for remote sensing and Earth observation (MIT, 4k+ stars)

Microsoft's AI-powered geospatial Earth science application for natural-language exploration, visualization, and analysis of 130+ satellite collections, with STAC integration, multi-agent backend, MCP server, and deployable React/FastAPI stack (MIT, 2025)

High-level open-source geospatial AI package for satellite/aerial imagery analysis, model training, inference, interactive visualization, and QGIS integration, bridging PyTorch/Transformers with remote sensing workflows (MIT, 2026)

Universal graph neural network framework for global-to-regional Earth system forecasting, combining multi-grid theory with a dynamic-system perspective to build multi-scale graphs that densify target regions for local high-frequency features; adaptive message passing with dynamic gating units is theoretically proven to act as high-pass filtering against over-smoothing, and a neural nested-grid method mitigates boundary information loss in high-resolution regional forecasts; extended to causally-coupled ocean-atmosphere cross-sphere modeling with strong extreme-event prediction, releasing inference/training code, pretrained weights, and preprocessed data (Renmin University & PolyU, 213+ stars, MIT License)

LLM agent framework for Earth Observation with 104 specialized tools across 5 functional kits

Minimal-modification vision transformer for skillful and reliable medium-range weather forecasting, introducing weather-specific patch embedding, randomized dynamics forecasting over varying time intervals, and pressure-weighted loss; competitive at short range and outperforming prior methods beyond 7 days on WeatherBench 2 with orders-of-magnitude less training data and compute, with favorable scaling in model size and training tokens (MIT License)

First foundation model for weather and climate by Microsoft, Vision Transformer-based architecture trained on heterogeneous datasets (ICML 2023)