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Development","sub_category":"Curated Lists","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Awesome-Earth-Artificial-Intelligence\n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)  [![GitHub stars](https://img.shields.io/github/stars/ESIPFed/Awesome-Earth-Artificial-Intelligence)](https://github.com/ESIPFed/Awesome-Earth-Artificial-Intelligence/stargazers) [![Chat on slack](https://img.shields.io/badge/slack-join-ff69b4.svg)](https://esip-slack-invite.herokuapp.com/) [![Twitter](https://img.shields.io/twitter/url?style=social\u0026url=https%3A%2F%2Fgithub.com%2FESIPFed%2FAwesome-Earth-Artificial-Intelligence)](https://twitter.com/intent/tweet?text=Wow:\u0026url=https%3A%2F%2Fgithub.com%2FESIPFed%2FAwesome-Earth-Artificial-Intelligence)\n\nA curated list of tutorials, notebooks, software, datasets, courses, books, video lectures and papers specifically for Artificial Intelligence (AI) use cases in Earth Science — with emphasis on open-source tools, freely accessible papers, and reproducible benchmarks (including geospatial and weather/climate foundation models).\n\nMaintained by ESIP Machine Learning Cluster. Free and open to inspire AI for Good.\n\nContributions are most welcome. Please refer to our [contributing guidelines](contributing.md), [what is awesome?](awesome.md), and [Code of Conduct](code-of-conduct.md).\n\n## Contents\n\n| | | | | |\n| - | - | - | - | - |\n| [Courses](#courses) | [Books](#books) | [Tools](#tools) | [Foundation Models](#foundation-models) | [Tutorials](#tutorials) |\n| [Training Datasets](#training-data) | [Code](#code) | [Videos](#videos) | [Papers](#papers) | [Reports](#reports) |\n| [Thoughts](#thoughts) | [Competitions](#competitions) | [Communities](#communities) | [RelatedAwesome](#relatedawesome) | |\n\n\n\n## ML-enthusiastic Earth Scientific Questions\n\n| Earth Spheres | Scientific Problems |\n| - | - |\n| Geosphere | \u003cul\u003e\u003cli\u003eHow to identify hidden signals of earthquakes?\u003c/li\u003e \u003cli\u003eHow to learn the spatio-temporal relationships amonog earthquakes and make predictions based on the relationship?\u003c/li\u003e \u003cli\u003eHow to capture complex relationships of volcano-seismic data and classify explosion quakes in volcanos?\u003c/li\u003e \u003cli\u003eHow to predict landslides\u003c/li\u003e \u003cli\u003eHow to estimate the damage?\u003c/li\u003e\u003c/ul\u003e |\n| Atmosphere | \u003cul\u003e\u003cli\u003eHow to trace and predict climate change using machine learning?\u003c/li\u003e\u003cli\u003eHow to predict hurricane?\u003c/li\u003e\u003cli\u003eHow to monitor and predict meteorological drought?\u003c/li\u003e\u003cli\u003eHow to detect wildfire early?\u003c/li\u003e\u003cli\u003eHow to monitor and predict air quality?\u003c/li\u003e\u003cli\u003eHow to predict dust storm?\u003c/li\u003e\u003cli\u003eHow to accelerate the model simulation and lower the computing costs?\u003c/li\u003e\u003c/ul\u003e |\n| Hydrosphere | \u003cul\u003e\u003cli\u003eHow to do high spatio-temporal resoluton waterbody mapping?\u003c/li\u003e\u003cli\u003eHow to get insights of water quality from remote sensing?\u003c/li\u003e\u003cli\u003eHow to monitor, and predict snow melt as a water resource?\u003c/li\u003e\u003c/ul\u003e |\n| Biosphere | \u003cul\u003e\u003cli\u003eHow to do high spatio-temporal resoluton forest mapping?\u003c/li\u003e\u003cli\u003eHow to do high spatio-temporal resoluton crop mapping?\u003c/li\u003e\u003cli\u003eHow to do high spatio-temporal resoluton animal mapping?\u003c/li\u003e\u003cli\u003eHow to fine-tune geospatial foundation models with sparse labels?\u003c/li\u003e\u003c/ul\u003e |\n| Cryosphere | \u003cul\u003e\u003cli\u003eHow to do high spatio-temporal resoluton mapping and classification of sea ice?\u003c/li\u003e\u003cli\u003eHow to monitor and predict glacier/ice sheet mass loss?\u003c/li\u003e\u003c/ul\u003e |\n| Cross-cutting | \u003cul\u003e\u003cli\u003eHow to benchmark geospatial foundation models reproducibly across sensors and tasks?\u003c/li\u003e\u003cli\u003eHow to combine physics-based models with machine learning for weather and climate?\u003c/li\u003e\u003cli\u003eHow to build trustworthy, uncertainty-aware AI for operational Earth science?\u003c/li\u003e\u003cli\u003eHow to use vision-language models for interactive Earth observation analysis?\u003c/li\u003e\u003c/ul\u003e |\n\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Courses\n\n* :sunglasses::sparkling_heart: [GeoSMART Machine Learning Curriculum](https://geo-smart.github.io/curriculum)\n\n* :sunglasses::sparkling_heart: [Introduction to Machine Learning for Earth Observation (EO College MOOC)](https://eo-college.org/courses/introduction-to-machine-learning-for-earth-observation/) - Free MOOC from TUM/DLR covering classification, object detection, change detection, SAR, and self-supervised learning for EO\n\n* :sunglasses::sparkling_heart: [GeoAI with Python: A Practical Guide to Open-Source Geospatial AI](https://github.com/giswqs/GeoAI-Book) [Zenodo](https://zenodo.org/records/19207014) - Open-access book with 23 chapters of executable code for segmentation, detection, change detection, and foundation models\n\n* [ICESat-2 Hackweek](https://icesat-2-2023.hackweek.io)\n\n* [ML Seminar: Physics-informed Machine learning for weather and climate science](https://www.youtube.com/watch?v=B_4TONeY75U) (57:35) by Dr. Karthik Kashinath from Lawrence Berkeley National Lab, Mar 19, 2021\n\n* [ML Seminar: Scalable Geospatial Analysis](https://www.youtube.com/watch?v=84VNWk_zFTM) (53:23) by Tom Augspurger from Microsoft AI for Earth, May 20, 2021 \n\n* [Fundamentals of ML and DL in Python](https://github.com/ageron/handson-ml) - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. \n\n* [Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School](https://www2.cisl.ucar.edu/tai4es) will be virtually the week of July 26-30, 2021.\n\n* [Artificial Intelligence for Earth System Science (AI4ESS) Summer School](https://www2.cisl.ucar.edu/events/summer-school/ai4ess/2020/artificial-intelligence-earth-system-science-ai4ess-summer-school) [repo](https://github.com/NCAR/ai4ess-hackathon-2020) [readinglist](https://www2.cisl.ucar.edu/sites/default/files/AI4ESS%20Webpage%20PDF%20Recommended%20Readings.pdf)\n\n* [American Meterological Survey AI Webinar Series](https://www.ametsoc.org/index.cfm/ams/webinar-directory/)\n\n* [USGS Artificial Intelligence/Machine Learning Workshop](https://my.usgs.gov/confluence/pages/viewpage.action?pageId=613780355)\n\n* [Stanford CS 229 ML Cheatsheets](https://github.com/afshinea/stanford-cs-229-machine-learning)\n\n* [RadiantEarth ML4EO Bootcamp 2021](https://github.com/RadiantMLHub/ml4eo-bootcamp-2021)\n\n* [Summer School on High-Performance and Disruptive Computing in Remote Sensing - Scaling Machine Learning for Remote Sensing using Cloud Computing](https://github.com/nasa-impact/workshop_notebooks)\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Books\n\n* :sunglasses: :sparkling_heart: [Artificial Intelligence in Earth Science](https://www.google.com/books/edition/Artificial_Intelligence_in_Earth_Science/iH-HEAAAQBAJ?hl=en\u0026gbpv=1\u0026printsec=frontcover)\n\n* :sunglasses: :sparkling_heart: [Artificial Intelligence Methods in the Environmental Sciences](https://books.google.com/books?id=0N4XBd5yl6oC\u0026printsec=frontcover\u0026source=gbs_ge_summary_r\u0026cad=0#v=onepage\u0026q\u0026f=false)\n\n* [Deep Learning for the Earth Sciences](https://books.google.com/books?id=Wd3gzgEACAAJ\u0026printsec=frontcover\u0026source=gbs_ge_summary_r\u0026cad=0#v=onepage\u0026q\u0026f=false)\n\n* [How to achieve AI maturity and why it matters? (PDF)](https://www.amdocs.com/sites/default/files/filefield_paths/ai-maturity-model-whitepaper.pdf)\n\n* [70-Years-of-Machine-Learning-in-Geoscience-in-Review](https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review)\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Tools\n\nEarth observation, geospatial, weather, and climate software only. Entries are sorted alphabetically by name. `:sunglasses:` marks maintainer picks for this list (not a quality tier). For pretrained model weights, see [Foundation Models](#foundation-models). For general ML infrastructure, see [RelatedAwesome](#relatedawesome).\n\n* [ai-models](https://github.com/ecmwf-lab/ai-models) - Open-source CLI to run AI weather models (GraphCast, FourCastNet, Pangu-Weather) with ECMWF data pipelines\n\n* [ClimateLearn](https://github.com/aditya-grover/climate-learn) [paper](https://arxiv.org/abs/2307.01909) - PyTorch library for weather forecasting and climate downscaling benchmarks (ERA5, CMIP6)\n\n* [EarthML](https://github.com/pyviz-topics/EarthML) [website](http://earthml.holoviz.org/) - Tools for working with machine learning in earth science\n\n* [eo-learn](https://github.com/sentinel-hub/eo-learn) - Earth observation processing framework for machine learning in Python\n\n* [GEO-Bench-2](https://github.com/The-AI-Alliance/GEO-Bench-2) [leaderboard](https://huggingface.co/spaces/aialliance/GEO-Bench-2-Leaderboard) [paper](https://arxiv.org/abs/2511.15658) - Reproducible benchmark for geospatial foundation models across 19 permissively licensed datasets\n\n* :sunglasses: [GeoAI](https://github.com/opengeos/geoai) [docs](https://opengeoai.org/) - Unified Python framework for EO deep learning: segmentation, detection, change detection, and foundation model workflows\n\n* [GRIME AI](https://github.com/GRIME-Lab/GRIME-AI/wiki) [website](https://gaugecam.org/) - Ecohydrological workflow suite for ground-based time-lapse imagery, from acquisition through ML applications\n\n* [GRIME2](https://github.com/gaugecam-dev/GRIME2/wiki) [website](https://gaugecam.org/) - Camera-based water level measurement from ground-based time-lapse imagery\n\n* [Makani](https://github.com/NVIDIA/makani) - Scalable training framework for ML weather models (FourCastNet 3); Apache 2.0\n\n* [Microsoft AI for Earth API Platform](https://github.com/microsoft/AIforEarth-API-Platform) - Distributed API hosting for long-running geospatial ML model inference on Azure/Kubernetes\n\n* :sunglasses: [pygeoweaver](https://github.com/ESIPFed/pygeoweaver) - Python library for AI \u0026 geospatial workflow management, FAIRness, tangibility and productivity improvement\n\n* [segment-geospatial (samgeo)](https://github.com/opengeos/segment-geospatial) [docs](https://samgeo.gishub.org/) - Segment Anything Model (SAM) and HQ-SAM for geospatial imagery segmentation\n\n* [SeisBench](https://github.com/seisbench/seisbench) [docs](https://seisbench.readthedocs.io/) - Open toolbox for earthquake ML: phase picking, event detection, pretrained models, and benchmark datasets\n\n* :sunglasses: [TerraTorch](https://github.com/IBM/terratorch) [paper](https://arxiv.org/abs/2503.20563) - Fine-tuning and benchmarking toolkit for geospatial foundation models; integrates with GEO-Bench-2 and Hugging Face weights\n\n* [torch-harmonics](https://github.com/NVIDIA/torch-harmonics) - Differentiable signal processing on the sphere for geometric weather ML; BSD-3-Clause\n\n* :sunglasses: [TorchGeo](https://github.com/torchgeo/torchgeo) [docs](https://torchgeo.readthedocs.io/) - PyTorch domain library with 100+ geospatial datasets, spatial samplers, multispectral transforms, and pretrained backbones\n\n* [WeatherBench 2](https://github.com/google-research/weatherbench2) [docs](https://weatherbench2.readthedocs.io/) - Open evaluation framework and leaderboard for data-driven global weather models\n\n* [Xarray-Beam](https://github.com/google/xarray-beam) - Python library for building Apache Beam pipelines with Xarray datasets\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Foundation Models\n\nPretrained model weights and primary model repositories for Earth observation, weather, and climate. Sorted alphabetically within each group. For fine-tuning toolkits and benchmarks, see [Tools](#tools). For task-specific application code and vision-language models, see [Code](#code).\n\n### Earth Observation\n\n* [AlphaEarth Foundations](https://arxiv.org/abs/2507.22291) [embeddings](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL) - Global 10 m embedding field layers (2017–2024) for sparse-label mapping; annual embeddings on Google Earth Engine and GCS\n\n* :sunglasses::sparkling_heart: [Clay](https://github.com/Clay-foundation/model) [docs](https://clay-foundation.github.io/model/) [weights](https://huggingface.co/made-with-clay/Clay) - Sensor-agnostic MAE foundation model (v1.5) for EO embeddings across Sentinel-2, Landsat, Sentinel-1, and custom sensors; Apache 2.0\n\n* [Copernicus-FM](https://github.com/zhu-xlab/Copernicus-FM) [paper](https://arxiv.org/abs/2503.11849) - Unified Copernicus foundation model across Sentinel missions with Copernicus-Pretrain and Copernicus-Bench\n\n* [DOFA](https://github.com/zhu-xlab/DOFA) [paper](https://arxiv.org/abs/2403.15356) - Dynamic One-For-All multimodal foundation model with wavelength-conditioned hypernetworks for cross-sensor generalization\n\n* :sunglasses::sparkling_heart: [Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0) [weights](https://huggingface.co/ibm-nasa-geospatial) [paper](https://arxiv.org/abs/2412.02732) - Multi-temporal ViT foundation model (300M/600M) trained on 4.2M global HLS time series at 30 m; fine-tune via [TerraTorch](#tools)\n\n* [TerraMind](https://github.com/ibm/terramind) [weights](https://huggingface.co/ibm-esa-geospatial) [paper](https://arxiv.org/abs/2504.11171) - Any-to-any generative multimodal EO foundation model (IBM/ESA Φ-lab); fine-tune via [TerraTorch](#tools)\n\n### Weather and Climate\n\n* [Aurora](https://github.com/microsoft/aurora) [docs](https://microsoft.github.io/aurora/) [paper](https://arxiv.org/abs/2405.13063) - 1.3B-parameter atmospheric foundation model for weather, air pollution, and ocean waves\n\n* [FourCastNet 3](https://arxiv.org/abs/2507.12144) - Probabilistic spherical-convolution weather ensemble forecasting at 0.25°; training via [Makani](#tools)\n\n* [GraphCast / GenCast](https://github.com/google-deepmind/graphcast) [GraphCast paper](https://arxiv.org/abs/2212.12794) [GenCast paper](https://arxiv.org/abs/2312.15796) - GNN-based medium-range global weather forecasting and diffusion ensemble forecasting; Apache 2.0\n\n* [NeuralGCM](https://github.com/neuralgcm/neuralgcm) [dycore](https://github.com/neuralgcm/dinosaur) [paper](https://arxiv.org/abs/2311.07222) - Differentiable hybrid general circulation model combining physics-based dynamics with learned components; Apache 2.0 code, CC BY-SA 4.0 weights\n\n* :sunglasses::sparkling_heart: [Prithvi-WxC](https://github.com/NASA-IMPACT/Prithvi-WxC) [weights](https://huggingface.co/Prithvi-WxC) [paper](https://arxiv.org/abs/2409.13598) - 2.3B-parameter weather/climate foundation model on MERRA-2 for forecasting, downscaling, and parameterization\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Tutorials\n\n* :sunglasses::sparkling_heart: [GeoSMART Machine Learning Curriculum \u0026 Use Cases](https://geo-smart.github.io/usecases)\n\n* :sunglasses::sparkling_heart: [GeoAI with Python Book Code](https://github.com/giswqs/GeoAI-Book) - Executable notebooks for seven core GeoAI tasks and foundation model workflows\n\n* :sunglasses::sparkling_heart: [GeoAI Video Tutorials](https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPcvENqwaPa_QwbbkZ5sctZE) [docs](https://opengeoai.org/) - Step-by-step GeoAI package tutorials from Open Geospatial Solutions\n\n* :sunglasses::sparkling_heart: [TerraTorch Documentation](https://terrastackai.github.io/terratorch/stable/) - Fine-tuning guides for Prithvi, TerraMind, Clay, and GEO-Bench-2 benchmarking\n\n* :sunglasses::sparkling_heart: [NeuralGCM Inference Quickstart](https://neuralgcm.readthedocs.io/en/latest/inference_demo.html) - Run pretrained hybrid GCM weather forecasts with open checkpoints on GCS\n\n* :sunglasses::sparkling_heart: [NASA Openscapes Earthdata Cloud Cookbook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/our-cookbook.html)\n\n* :sunglasses::sparkling_heart: [Artificial Intelligence in Earth science Book Materials](https://github.com/earth-artificial-intelligence/earth_ai_book_materials)\n\n* :sunglasses::sparkling_heart: [RadiantEarth MLhub Tutorials](https://github.com/radiantearth/mlhub-tutorials)\n\n* [Machine Learning Tutorials (general, not Earth science specific)](https://github.com/ethen8181/machine-learning)\n\n* [Pixel-level land cover classification](https://github.com/Azure/pixel_level_land_classification)\n\n* [EO-learn-workshop](https://github.com/sentinel-hub/eo-learn-workshop) - EO-learn-workshop: Bridging Earth Observation data and Machine Learning in Python, \n\n* [Machine Learning for Development](https://github.com/worldbank/ml4dev) Machine Learning for Development: A method to Learn and Identify Earth Features from Satellite Images, \n\n* [ELSI-DL-Bootcamp](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp) - Intro to Machine Learning and Deep Learning for Earth-Life Sciences, \n\n* [UW WaterhackerWeek](https://github.com/waterhackweek/whw2020_machine_learning) - Introduction to Machine Learning on Landslide Data and Scikit-learn from [UW WaterhackerWeek](https://waterhackweek.github.io/), \n\n* [Planet Snow Mapping](https://github.com/acannistra/planet-snowcover) - Introduction to using Planet imagery to map snow cover\n\n* [Machine Learning Pipeline for Climate Science](https://ml-clim.github.io/drought-prediction/) - an end-to-end pipeline for the creation, intercomparison and evaluation of machine learning methods in climate science\n\n* [AI Cheatsheets](https://github.com/kailashahirwar/cheatsheets-ai) - Essential Cheat Sheets for deep learning and machine learning engineers. It contains a lot of useful tutorials to learn awesome tricks on AI engineering\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Training Data\n\n* [Kaggle Earth Science Training Dataset](https://www.kaggle.com/search?q=tag%3A%22earth+science%22+in%3Adatasets)\n\n* [Radiant MLHub](https://www.mlhub.earth/#datasets) - an open library for geospatial training data\n\n* [Google Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets/catalog)\n\n* [AlphaEarth Satellite Embeddings](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL) [paper](https://arxiv.org/abs/2507.22291) - Global annual 10 m embedding fields (2017–2024) from AlphaEarth Foundations; also on [GCS](https://console.cloud.google.com/storage/browser/alphaearth_foundations)\n\n* [GEO-Bench-2 Datasets](https://github.com/The-AI-Alliance/GEO-Bench-2) - 19 permissively licensed benchmark datasets for geospatial foundation model evaluation on Hugging Face\n\n* [Copernicus-Embed-025deg](https://github.com/zhu-xlab/Copernicus-FM) - Global 0.25° embedding map integrating multi-source Sentinel observations (released with Copernicus-FM)\n\n* [WeatherBench 2 ERA5 Zarr](https://github.com/google-research/weatherbench2) - Open cloud-optimized ERA5 and baseline forecast data for ML weather model training and evaluation\n\n* [University of California Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)\n\n* [EuroSAT Dataset](https://github.com/phelber/EuroSAT) - EuroSAT Dataset: Land Use and Land Cover Classification with Sentinel-2, \n\n* [Awesome Satellite Imagery Datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets) - Awesome Satellite Imagery Datasets: A curated list of deep learning training datasets, \n\n* [STanford EArthquake Dataset (STEAD)](https://github.com/smousavi05/STEAD) - A Global Data Set of Seismic Signals for AI\n\n* [ZipCheckup](https://zipcheckup.com) - Free ZIP-level environmental safety dataset for 42,000+ US ZIP codes covering water quality, air quality, PFAS contamination, radon, lead, flood risk, and 11 additional verticals. Public REST API and npm/PyPI packages for ML pipelines. CC BY 4.0.\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Code\n\nTask-specific implementations and Earth-facing applications. Foundation model weights live under [Foundation Models](#foundation-models); fine-tuning toolkits under [Tools](#tools).\n\n* :sunglasses::sparkling_heart: [Earth System Emulator (ESEm)](https://github.com/duncanwp/ESEm) - A tool for emulating geophysical datasets including (but not limited to) Earth System Models\n\n* :sunglasses::sparkling_heart: [EmissionAI](https://github.com/ZihengSun/EmissionAI) - Microsoft AI for Earth Project: AI Monitoring Coal-fired Power Plant Emission from Space\n\n* [EarthDial](https://github.com/hiyamdebary/EarthDial) [paper](https://arxiv.org/abs/2412.15190) - Multi-spectral, multi-temporal vision-language model for EO dialogue across 44 downstream datasets\n\n* [GeoChat](https://github.com/mbzuai-oryx/GeoChat) [paper](https://arxiv.org/abs/2311.15826) - Grounded large vision-language model for remote sensing QA, captioning, and referring detection\n\n* [Global Forest Watch](https://www.globalforestwatch.org/) - ML-powered deforestation and forest cover change monitoring from satellite imagery\n\n* [iNaturalist Computer Vision](https://www.inaturalist.org/pages/computer_vision_demo) - Species identification from community-contributed observations (76,000+ taxa)\n\n* [TEOChat](https://github.com/ermongroup/TEOChat) [paper](https://arxiv.org/abs/2410.06234) - Temporal vision-language assistant for change detection, damage assessment, and EO dialogue\n\n* [Wildlife Insights](https://www.wildlifeinsights.org/) - Automated species identification from camera trap images; integrates with GBIF\n\n* [BassNet](https://github.com/hbutsuak95/BASS-Net),[paper-preprint](https://arxiv.org/abs/1612.00144) - Deep Learning for Land-cover Classification in Hyperspectral Images, \n\n* [MTLCC](https://github.com/TUM-LMF/MTLCC) - Multitemporal Land Cover Classification Network (ConvLSTM, ConvGRU), \n\n* [Landsat Time Series Analysis for Multi-Temporal Land Cover Classification](https://github.com/agr-ayush/Landsat-Time-Series-Analysis-for-Multi-Temporal-Land-Cover-Classification)\n\n* [EarthEngine-Deep-Learning](https://github.com/ucalyptus/EarthEngine-Deep-Learning) - Deep Learning on Google Earth Engine, \n\n* [Continuous Change Detection and Classification](https://github.com/GERSL/CCDC) - Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data, \n\n* [Object-based Classification on Earth Engine](https://github.com/GERSL/CCDC) - Object-based land cover classification with Feature Extraction and Feature Selection for Google Earth Engine (GEE), \n\n* [Earth Lens](https://github.com/microsoft/Earth-Lens) - Earth Lens, a Microsoft Garage project is an iOS iPad application that helps people and organizations quickly identify and classify objects in aerial imagery through the power of machine learning. \n\n* [Image Classification Neural Network Ranking with source code](https://paperswithcode.com/task/image-classification) - paperswithcode has put together a list of cutting-edge papers and ranked them with the claimed accuracy.\n\n* [EQTransformer](https://github.com/smousavi05/EQTransformer) - An AI-Based Earthquake Signal Detector and Phase Picker. \n\n* [Tropical Cyclone Windspeed Estimator](https://github.com/drivendataorg/wind-dependent-variables) - Winning solutions for Tropical Cyclone Wind Speed Prediction Competition\n\n* [Infernis](https://github.com/argonBIsystems/infernis) - Open-source ML-powered wildfire risk prediction engine for British Columbia. XGBoost + CNN trained on 10 fire seasons (2015-2024) from 21 open government and scientific data sources. Provides a free [REST API](https://infernis.ca/v1/docs) with daily predictions at 5km resolution.\n\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Videos\n\n* [GeoAI Tutorials Playlist](https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPcvENqwaPa_QwbbkZ5sctZE) - Open Geospatial Solutions tutorials on segmentation, detection, and QGIS GeoAI plugin workflows\n\n* [Tutorial on Microsoft Azure Machine Learning Studio (AutoML-Regression)](https://www.youtube.com/watch?v=ip5GHTMZPhA), created by Microsoft AI for Earth Project: AI Monitoring Coal-fired Power Plant Emission from Space.\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Papers\n\n* :sunglasses: :sparkling_heart: [A Review of Earth Artificial Intelligence](https://www.sciencedirect.com/science/article/pii/S0098300422000036)\n\n* :sunglasses: :sparkling_heart: [Foundation Models for Remote Sensing and Earth Observation: A Survey](https://arxiv.org/abs/2410.16602) - Taxonomy of visual, vision-language, and LLM-based RSFMs with benchmarking across public datasets\n\n* [Towards practical artificial intelligence in Earth sciences](https://link.springer.com/article/10.1007/s10596-024-10317-7)\n\n* [A Review of Practical AI for Remote Sensing in Earth Sciences](https://www.mdpi.com/2072-4292/15/16/4112)\n\n* [Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications](https://arxiv.org/abs/2412.02732)\n\n* [Prithvi WxC: Foundation Model for Weather and Climate](https://arxiv.org/abs/2409.13598)\n\n* [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://arxiv.org/abs/2504.11171)\n\n* [TerraTorch: The Geospatial Foundation Models Toolkit](https://arxiv.org/abs/2503.20563)\n\n* [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://arxiv.org/abs/2507.22291)\n\n* [GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI](https://arxiv.org/abs/2511.15658)\n\n* [Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities (DOFA)](https://arxiv.org/abs/2403.15356)\n\n* [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](https://arxiv.org/abs/2507.12144)\n\n* [GraphCast: Learning skillful medium-range global weather forecasting](https://arxiv.org/abs/2212.12794)\n\n* [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://arxiv.org/abs/2312.15796)\n\n* [Aurora: A Foundation Model of the Atmosphere](https://arxiv.org/abs/2405.13063)\n\n* [Neural General Circulation Models for Weather and Climate](https://arxiv.org/abs/2311.07222)\n\n* [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://arxiv.org/abs/2410.06234)\n\n* [GeoChat: Grounded Large Vision-Language Model for Remote Sensing](https://arxiv.org/abs/2311.15826)\n\n* [EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues](https://arxiv.org/abs/2412.15190)\n\n* [Towards a Unified Copernicus Foundation Model for Earth Vision](https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper.html) [arxiv](https://arxiv.org/abs/2503.11849)\n\n* [Advances on Multimodal Remote Sensing Foundation Models for Earth Observation Downstream Tasks: A Survey](https://www.mdpi.com/2072-4292/17/21/3532) - Open-access review of vision-X multimodal RSFMs\n\n* [Big Earth data analytics: A survey](https://www.tandfonline.com/doi/full/10.1080/20964471.2019.1611175)\n\n* [Adoption of machine learning techniques in ecology and earth science](https://oneecosystem.pensoft.net/article/8621/download/pdf/)\n\n* [CIRA Guide To Custom Loss Functions For Neural Networks In Environmental Sciences - Version 1](https://arxiv.org/pdf/2106.09757.pdf)\n\n* [Zero-Shot Learning of Aerosol Optical Properties with Graph NeuralNetworks](https://arxiv.org/pdf/2107.10197.pdf)\n\n* [NeuralHydrology - a collection of papers on using neural networks in hydrology](https://neuralhydrology.github.io/)\n\n* [Ten Ways to Apply Machine Learning in Earth and Space Sciences](https://eos.org/opinions/ten-ways-to-apply-machine-learning-in-earth-and-space-sciences)\n\n* [Advancing AI for Earth Science: A Data Systems Perspective](https://eos.org/science-updates/advancing-ai-for-earth-science-a-data-systems-perspective)\n\n* [Google Earth Engine: Planetary-scale geospatial analysis for everyone](https://www.sciencedirect.com/science/article/pii/S0034425717302900)\n\n* [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://arxiv.org/abs/2308.15560)\n\n* [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://arxiv.org/abs/2307.01909)\n\n* [PCA-OS: A Planetary Climate Adaptation Operating System](https://chaoyue0307.github.io/PCA-OS/) (KDD 2026 Blue Sky Ideas Track) - Frames climate adaptation as a continual learning and decision loop over an intervention-aware global causal knowledge graph, fusing Earth-observation signals and operational traces into versioned, auditable adaptation interventions and robust decision portfolios.\n\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Reports\n\n* [Workshop Report: Advancing Application of Machine Learning Tools for NASA’s Earth Observation Data](https://cdn.earthdata.nasa.gov/conduit/upload/14287/NASA_ML_Workshop_Report.pdf) \n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Thoughts\n\n* :sunglasses: :sparkling_heart: [Learning earth system models from observations: machine learning or data assimilation?](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0089)\n\n* [Artificial intelligence: A powerful paradigm for scientific research](https://www.sciencedirect.com/science/article/pii/S2666675821001041)\n\n* [Why 90% of machine learning models never hit the market](https://thenextweb.com/news/why-most-machine-learning-models-never-hit-market-syndication)\n\n* ['Farewell Convolutions' – ML Community Applauds Anonymous ICLR 2021 Paper That Uses Transformers for Image Recognition at Scale](https://syncedreview.com/2020/10/08/farewell-convolutions-ml-community-applauds-anonymous-iclr-2021-paper-that-uses-transformers-for-image-recognition-at-scale/)\n\n* [37 reasons why your neural network is not working](https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607)\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Competitions\n\n* :sunglasses::sparkling_heart: [GeoAI Challenge](https://aiforgood.itu.int/about-ai-for-good/geoai-challenge/) - aimed at providing solutions for collaboratively addressing real-world geospatial problems by applying artificial intelligence (AI)/machine learning (ML)\n\n* [2025 GeoAI Challenge: Cropland Mapping in Dry Environments](https://zindi.africa/competitions/geoai-challenge-for-cropland-mapping-in-dry-environments) - ITU/FAO challenge on distinguishing cropland from pasture in Fergana and Orenburg using time-series satellite imagery\n\n* [2026 GeoAI Challenge: Reaching new heights with GeoFM](https://aiforgood.itu.int/about-us/geoai-challenge/) - ITU/ESA Φ-lab challenge on global surface height and land-cover mapping with open satellite imagery and GeoFM embeddings\n\n* [GPU Hackthons](https://www.gpuhackathons.org/) - designed to help scientists, researchers and developers to accelerate and optimize their applications on GPUs.\n\n* [LANL Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction)\n\n* [HackerEarth](https://www.hackerearth.com/challenges/)\n\n\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## Communities\n\n* [ESIP Machine Learning Cluster](https://wiki.esipfed.org/Machine_Learning)\n\n* [ESIP Agriculture and Climate Cluster](https://wiki.esipfed.org/Agriculture_and_Climate)\n\n* [AI Alliance Climate \u0026 Sustainability Group](https://thealliance.ai/blog/geo-bench-2-from-performance-to-capability-rethinking-evaluation-in-geospatial-ai) - Community behind GEO-Bench-2 and open geospatial foundation model evaluation\n\n* [TorchGeo Community](https://torchgeo.org/) - OSGeo community project for geospatial deep learning in PyTorch\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n## RelatedAwesome\n- [Awesome-Open-Geoscience](https://github.com/softwareunderground/awesome-open-geoscience) – ![Awesome](media/icon/awesome.png) A list is curated from repositories that make our lives as geoscientists, hackers and data wranglers easier or just more awesome. In accordance with the awesome manifesto, we add awesome repositories.\n- [Awesome-Spatial](https://github.com/RoboDonut/awesome-spatial) – ![Awesome](media/icon/awesome.png) Awesome list for geospatial, not specific to geoscience but significant overlap\n- [Awesome Open Climate Science](https://github.com/pangeo-data/awesome-open-climate-science) – ![Awesome](media/icon/awesome.png) Awesome list for atmospheric, ocean, climate, and hydrologic science\n- [awesome-weather-models](https://github.com/rebase-energy/awesome-weather-models) – ![Awesome](media/icon/awesome.png) Catalogue of AI-based weather forecasting models with open-source and open-weights status\n- [awesome-WeatherAI](https://github.com/HeQinWill/awesome-WeatherAI) – ![Awesome](media/icon/awesome.png) Papers, datasets, and open model implementations for AI weather and climate\n- [Awesome_AI4Earth](https://github.com/taohan10200/Awesome_AI4Earth) – ![Awesome](media/icon/awesome.png) Deep learning for Earth system science, especially data-driven weather prediction\n- [Awesome-AI-for-Atmosphere-and-Ocean](https://github.com/XiongWeiTHU/Awesome-AI-for-Atmosphere-and-Ocean) – ![Awesome](media/icon/awesome.png) Research papers on AI for atmospheric science and oceanography\n- [Awesome Coastal](https://github.com/chrisleaman/awesome-coastal) – ![Awesome](media/icon/awesome.png) Awesome list for coastal engineers and scientists\n- [Awesome Satellite Imagery Datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets) - ![Awesome](media/icon/awesome.png) List of aerial and satellite imagery datasets with annotations for computer vision and deep learning\n- [Awesome Workflow Engines](https://github.com/meirwah/awesome-workflow-engines) - ![Awesome](media/icon/awesome.png) A curated list of awesome open source workflow engines\n- [Awesome Pipeline](https://github.com/pditommaso/awesome-pipeline) - ![Awesome](media/icon/awesome.png) A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin\n- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) - ![Awesome](media/icon/awesome.png) A curated list of awesome Machine Learning frameworks, libraries and software\n\n### General ML infrastructure (companion tools)\n\nThese are useful in Earth AI workflows but are not Earth-specific; we list them here rather than in [Tools](#tools).\n\n- [BentoML](https://github.com/bentoml/BentoML) – Open-source framework for high-performance ML model serving\n- [Dopamine](https://github.com/google/dopamine) – Research framework for reinforcement learning prototyping\n- [flashlight](https://github.com/facebookresearch/flashlight) – C++ standalone library for machine learning\n- [MindsDB](https://github.com/mindsdb/mindsdb) – Explainable AutoML framework on PyTorch\n- [Netron](https://github.com/lutzroeder/netron) – Neural network and ONNX/Keras/TFLite model visualizer\n- [ml.js](https://github.com/mljs/ml) – Machine learning tools in JavaScript\n- [MLflow](https://github.com/mlflow/mlflow) – Machine learning lifecycle platform\n- [OneFlow](https://github.com/Oneflow-Inc/oneflow) – Performance-centered open-source deep learning framework\n- [Polyaxon](https://github.com/polyaxon/polyaxon) – ML platform for Kubernetes training and monitoring\n- [Snips NLU](https://github.com/snipsco/snips-nlu) – Natural language understanding for structured extraction from text\n- [SynapseML](https://github.com/microsoft/SynapseML) – Scalable ML pipelines on Apache Spark\n- [TensorFlow Hub](https://github.com/tensorflow/hub) – Repository of reusable TensorFlow SavedModels\n- [TransmogrifAI](https://github.com/salesforce/TransmogrifAI) – AutoML library on Apache Spark (Scala)\n\n| ▲ [Top](#awesome-earth-artificial-intelligence) |\n| --- |\n\n  \n\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/77179","html_url":"https://ost.ecosyste.ms/projects/77179"}