awesome-earth-artificial-intelligence
github.com/esipfed/awesome-earth-artificial-intelligence ↗A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me weather and climate resources from awesome-earth-artificial-intelligence"
Installation instructions →What's inside
Competitions
- 2025 GeoAI Challenge: Cropland Mapping in Dry Environments
ITU/FAO challenge on distinguishing cropland from pasture in Fergana and Orenburg using time-series satellite imagery
- 2026 GeoAI Challenge: Reaching new heights with GeoFM
ITU/ESA Φ-lab challenge on global surface height and land-cover mapping with open satellite imagery and GeoFM embeddings
- GeoAI Challenge
aimed at providing solutions for collaboratively addressing real-world geospatial problems by applying artificial intelligence (AI)/machine learning (ML)
- GPU Hackthons
designed to help scientists, researchers and developers to accelerate and optimize their applications on GPUs.
- HackerEarth
- LANL Earthquake Prediction
Thoughts
- 37 reasons why your neural network is not working
- Artificial intelligence: A powerful paradigm for scientific research
- 'Farewell Convolutions' – ML Community Applauds Anonymous ICLR 2021 Paper That Uses Transformers for Image Recognition at Scale
- Learning earth system models from observations: machine learning or data assimilation?
- Why 90% of machine learning models never hit the market
Books
Papers
- Adoption of machine learning techniques in ecology and earth science
- Advances on Multimodal Remote Sensing Foundation Models for Earth Observation Downstream Tasks: A Survey
Open-access review of vision-X multimodal RSFMs
- Advancing AI for Earth Science: A Data Systems Perspective
- AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
- A Review of Earth Artificial Intelligence
- A Review of Practical AI for Remote Sensing in Earth Sciences
Tools
- AI Agent by TerraLab
QGIS plugin: an AI agent that takes a plain-language task, finds the data, runs the analysis and builds the map, with one-click undo
- AI Edit by TerraLab
QGIS plugin: edit aerial and satellite imagery from a text prompt (land cover, building extraction, object removal, flood simulation), georeferenced output
- ai-models
Open-source CLI to run AI weather models (GraphCast, FourCastNet, Pangu-Weather) with ECMWF data pipelines
- AI Segmentation by TerraLab
QGIS plugin for point-and-click segmentation of buildings, trees and any object in satellite and drone imagery into vector polygons, with a free CPU-only local mode
- ClimateLearn
PyTorch library for weather forecasting and climate downscaling benchmarks (ERA5, CMIP6)
- EarthML
Tools for working with machine learning in earth science
Communities
- AI Alliance Climate & Sustainability Group
Community behind GEO-Bench-2 and open geospatial foundation model evaluation
- ESIP Agriculture and Climate Cluster
- ESIP Machine Learning Cluster
- TorchGeo Community
OSGeo community project for geospatial deep learning in PyTorch
Tutorials
- AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning engineers. It contains a lot of useful tutorials to learn awesome tricks on AI engineering
- Artificial Intelligence in Earth science Book Materials
- ELSI-DL-Bootcamp
Intro to Machine Learning and Deep Learning for Earth-Life Sciences,
- EO-learn-workshop
EO-learn-workshop: Bridging Earth Observation data and Machine Learning in Python,
- GeoAI with Python Book Code
Executable notebooks for seven core GeoAI tasks and foundation model workflows
- GeoSMART Machine Learning Curriculum & Use Cases
Training Data
- AlphaEarth Satellite Embeddings
Global annual 10 m embedding fields (2017–2024) from AlphaEarth Foundations; also on
- Copernicus-Embed-025deg
Global 0.25° embedding map integrating multi-source Sentinel observations (released with Copernicus-FM)
- EuroSAT Dataset
EuroSAT Dataset: Land Use and Land Cover Classification with Sentinel-2,
- GEO-Bench-2 Datasets
19 permissively licensed benchmark datasets for geospatial foundation model evaluation on Hugging Face
- Google Earth Engine Data Catalog
- Kaggle Earth Science Training Dataset
Showing a sample of 165 resources. View the full list on GitHub →