Find open-source science resources

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

Curated list of large weather models for AI Earth science

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)

Climate data benchmark for ML models

Weather prediction benchmark

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