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https://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.
https://github.com/ESIPFed/Awesome-Earth-Artificial-Intelligence

List: Awesome-Earth-Artificial-Intelligence

air-quality awesome-list biosphere datasets deep-learning dust earth-science earthquakes geosphere glacier hydrology land-cover-classification machine-learning snow volcano

Last synced: about 2 months ago
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A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.

Host: GitHub
URL: https://github.com/ESIPFed/Awesome-Earth-Artificial-Intelligence
Owner: ESIPFed
License: cc0-1.0
Created: 2020-08-13T20:04:12.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2023-06-11T18:05:05.000Z (about 1 year ago)
Last Synced: 2024-04-14T15:23:29.281Z (3 months ago)
Topics: air-quality, awesome-list, biosphere, datasets, deep-learning, dust, earth-science, earthquakes, geosphere, glacier, hydrology, land-cover-classification, machine-learning, snow, volcano
Homepage:
Size: 175 KB
Stars: 183
Watchers: 19
Forks: 46
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
- Code of conduct: code-of-conduct.md

Lists

awesome-open-geoscience - Awesome Earth Artificial Intelligence
open-sustainable-technology - Awesome-Earth-Artificial-Intelligence - A curated list of tutorials, notebooks, software, datasets, courses, books, video lectures and papers specifically for Artificial Intelligence use cases in Earth Science. (Sustainable Development / Curated Lists)
awesome-open-geoscience - Awesome Earth Artificial Intelligence
awesome-stars - 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. (Others)
ultimate-awesome - 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. (Other Lists / Julia Lists)

README

        # Awesome-Earth-Artificial-Intelligence

[![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&url=https%3A%2F%2Fgithub.com%2FESIPFed%2FAwesome-Earth-Artificial-Intelligence)](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FESIPFed%2FAwesome-Earth-Artificial-Intelligence)

A curated list of tutorials, notebooks, software, datasets, courses, books, video lectures and papers specifically for Artificial Intelligence (AI) use cases in Earth Science.

Maintained by ESIP Machine Learning Cluster. Free and open to inspire AI for Good.

Contributions are most welcome. Please refer to our [contributing guidelines](contributing.md), [what is awesome?](awesome.md), and [Code of Conduct](code-of-conduct.md).

## Contents

| | | | | |

| - | - | - | - | - |

| [Courses](#courses) | [Books](#books) | [Tools](#tools) | [Tutorials](#tutorials) | [Training Datasets](#traningdata) |

| [Code](#code) | [Videos](#videos) | [Papers](#papers) | [Reports](#reports) |  [Thoughts](#thoughts) |

| [Competitions](#competitions) | [Communities](#communities) | [RelatedAwesome](#RelatedAwesome) |

## ML-enthusiastic Earth Scientific Questions

| Earth Spheres | Scientific Problems |

| - | - |

| Geosphere | 


How to identify hidden signals of earthquakes?
 How to learn the spatio-temporal relationships amonog earthquakes and make predictions based on the relationship?
 How to capture complex relationships of volcano-seismic data and classify explosion quakes in volcanos?
 How to predict landslides
 How to estimate the damage?

 |

| Atmosphere | 

How to trace and predict climate change using machine learning?

How to predict hurricane?

How to monitor and predict meteorological drought?

How to detect wildfire early?

How to monitor and predict air quality?

How to predict dust storm?

How to accelerate the model simulation and lower the computing costs?

 |

| Hydrosphere | 

How to do high spatio-temporal resoluton waterbody mapping?

How to get insights of water quality from remote sensing?

How to monitor, and predict snow melt as a water resource?

 |

| Biosphere | 

How to do high spatio-temporal resoluton forest mapping?

How to do high spatio-temporal resoluton crop mapping?

How to do high spatio-temporal resoluton animal mapping?

 |

| Cryosphere | 

How to do high spatio-temporal resoluton mapping and classification of sea ice?

How to monitor and predict glacier/ice sheet mass loss?

 |

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## Courses

* :sunglasses::sparkling_heart: [GeoSMART Machine Learning Curriculum](https://geo-smart.github.io/curriculum)

* :sunglasses::sparkling_heart: [ICESat-2 Hackweek](https://icesat-2-2023.hackweek.io)

* [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

* [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 

* [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. 

* [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.

* [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)

* [American Meterological Survey AI Webinar Series](https://www.ametsoc.org/index.cfm/ams/webinar-directory/)

* [USGS Artificial Intelligence/Machine Learning Workshop](https://my.usgs.gov/confluence/pages/viewpage.action?pageId=613780355)

* [Stanford CS 229 ML Cheatsheets](https://github.com/afshinea/stanford-cs-229-machine-learning)

* [RadiantEarth ML4EO Bootcamp 2021](https://github.com/RadiantMLHub/ml4eo-bootcamp-2021)

* [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)

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## Books

* :sunglasses: :sparkling_heart: [Artificial Intelligence in Earth Science](https://www.google.com/books/edition/Artificial_Intelligence_in_Earth_Science/iH-HEAAAQBAJ?hl=en&gbpv=1&printsec=frontcover)

* :sunglasses: :sparkling_heart: [Artificial Intelligence Methods in the Environmental Sciences](https://books.google.com/books?id=0N4XBd5yl6oC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false)

* [Deep Learning for the Earth Sciences](https://books.google.com/books?id=Wd3gzgEACAAJ&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false)

* [How to achieve AI maturity and why it matters? (PDF)](https://www.amdocs.com/sites/default/files/filefield_paths/ai-maturity-model-whitepaper.pdf)

* [70-Years-of-Machine-Learning-in-Geoscience-in-Review](https://github.com/JesperDramsch/70-Years-of-Machine-Learning-in-Geoscience-in-Review)

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## Tools

* [eo-learn](https://github.com/sentinel-hub/eo-learn): Earth observation processing framework for machine learning in Python,

* [EarthML](https://github.com/pyviz-topics/EarthML) [website](http://earthml.holoviz.org/):  Tools for working with machine learning in earth science, 

* [ML visualization tool](https://github.com/lutzroeder/netron) - A Visualization tool for neural network, deep learning and machine learning models, support ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb), Darknet (.cfg), MXNet (.model, -symbol.json), Barracuda (.nn), ncnn (.param), Tengine (.tmfile), TNN (.tnnproto), UFF (.uff) and TensorFlow Lite (.tflite). 

* [Dopamine](https://github.com/google/dopamine)  is a research framework for fast prototyping of reinforcement learning algorithms, 

* [mlflow](https://github.com/mlflow/mlflow) - MLflow: A Machine Learning Lifecycle Platform, 

* [Snips NLU](https://github.com/snipsco/snips-nlu) Snips NLU (Natural Language Understanding) is a Python library that allows to extract structured information from sentences written in natural language. 

* [MindsDB](https://github.com/mindsdb/mindsdb) - MindsDB is an Explainable AutoML framework for developers built on top of Pytorch. It enables you to build, train and test state of the art ML models in as simple as one line of code. 

* [TensorFlow Hub](https://github.com/tensorflow/hub) TensorFlow Hub is a repository of reusable assets for machine learning with TensorFlow. In particular, it provides pre-trained SavedModels that can be reused to solve new tasks with less training time and less training data. 

* [Polyaxon](https://github.com/polyaxon/polyaxon) - Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. A Machine Learning Platform for Kubernetes. 

* [SynapseML](https://github.com/microsoft/SynapseML) - SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. Microsoft Machine Learning for Apache Spark, 

* [TransmogrifAI](https://github.com/salesforce/TransmogrifAI) - TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Apache Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. 

* [Microsoft AI for Earth API Platform](https://github.com/microsoft/AIforEarth-API-Platform) - Microsoft AI for Earth API Platform is a distributed infrastructure designed to provide a secure, scalable, and customizable API hosting, designed to handle the needs of long-running/asynchronous machine learning model inference. It is based on Azure and Kubernetes. 

* [OneFlow](https://github.com/Oneflow-Inc/oneflow) - OneFlow is a performance-centered and open-source deep learning framework. 

* [ml.js](https://github.com/mljs/ml) - ml.js - Machine learning tools in JavaScript. 

* [BentoML](https://github.com/bentoml/BentoML) - BentoML is an open-source framework for high-performance ML model serving. 

* [flashflight:](https://github.com/facebookresearch/flashlight) - flashflight: A C++ standalone library for machine learning. 

* [Xarray-Beam](https://github.com/google/xarray-beam) - Python library for building Apache Beam pipelines with Xarray datasets.

* :sunglasses: [pygeoweaver](https://github.com/ESIPFed/pygeoweaver) - Python library for AI & geospatial workflow management, FAIRness, tangibility and productivity improvement

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## Tutorials

* :sunglasses::sparkling_heart: [GeoSMART Machine Learning Curriculum & Use Cases](https://geo-smart.github.io/usecases)

* :sunglasses::sparkling_heart: [NASA Openscapes Earthdata Cloud Cookbook](https://nasa-openscapes.github.io/earthdata-cloud-cookbook/our-cookbook.html)

* :sunglasses::sparkling_heart: [Artificial Intelligence in Earth science Book Materials](https://github.com/earth-artificial-intelligence/earth_ai_book_materials)

* :sunglasses::sparkling_heart: [RadiantEarth MLhub Tutorials](https://github.com/radiantearth/mlhub-tutorials)

* [Machine Learning Tutorials (general, not Earth science specific)](https://github.com/ethen8181/machine-learning)

* [Pixel-level land cover classification](https://github.com/Azure/pixel_level_land_classification)

* [EO-learn-workshop](https://github.com/sentinel-hub/eo-learn-workshop) - EO-learn-workshop: Bridging Earth Observation data and Machine Learning in Python, 

* [Machine Learning for Development](https://github.com/worldbank/ml4dev) Machine Learning for Development: A method to Learn and Identify Earth Features from Satellite Images, 

* [ELSI-DL-Bootcamp](https://github.com/Machine-Learning-Tokyo/ELSI-DL-Bootcamp) - Intro to Machine Learning and Deep Learning for Earth-Life Sciences, 

* [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/), 

* [Planet Snow Mapping](https://github.com/acannistra/planet-snowcover) - Introduction to using Planet imagery to map snow cover

* [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

* [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

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## Training Data

* [Kaggle Earth Science Training Dataset](https://www.kaggle.com/search?q=tag%3A%22earth+science%22+in%3Adatasets)

* [Radiant MLHub](https://www.mlhub.earth/#datasets) - an open library for geospatial training data

* [Google Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets/catalog)

* [University of California Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)

* [EuroSAT Dataset](https://github.com/phelber/EuroSAT) - EuroSAT Dataset: Land Use and Land Cover Classification with Sentinel-2, 

* [Awesome Satellite Imagery Datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets) - Awesome Satellite Imagery Datasets: A curated list of deep learning training datasets, 

* [STanford EArthquake Dataset (STEAD)](https://github.com/smousavi05/STEAD) - A Global Data Set of Seismic Signals for AI

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## Code

* :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

* :sunglasses::sparkling_heart: [EmissionAI](https://github.com/ZihengSun/EmissionAI) - Microsoft AI for Earth Project: AI Monitoring Coal-fired Power Plant Emission from Space

* [BassNet](https://github.com/hbutsuak95/BASS-Net),[paper-preprint](https://arxiv.org/abs/1612.00144) - Deep Learning for Land-cover Classification in Hyperspectral Images, 

* [MTLCC](https://github.com/TUM-LMF/MTLCC) - Multitemporal Land Cover Classification Network (ConvLSTM, ConvGRU), 

* [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)

* [EarthEngine-Deep-Learning](https://github.com/ucalyptus/EarthEngine-Deep-Learning) - Deep Learning on Google Earth Engine, 

* [Continuous Change Detection and Classification](https://github.com/GERSL/CCDC) - Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data, 

* [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), 

* [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. 

* [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.

* [EQTransformer](https://github.com/smousavi05/EQTransformer) - An AI-Based Earthquake Signal Detector and Phase Picker. 

* [Tropical Cyclone Windspeed Estimator](https://github.com/drivendataorg/wind-dependent-variables) - Winning solutions for Tropical Cyclone Wind Speed Prediction Competition

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## Videos

* [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.

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## Papers

* :sunglasses: :sparkling_heart: [A Review of Earth Artificial Intelligence](https://www.sciencedirect.com/science/article/pii/S0098300422000036)

* [Big Earth data analytics: A survey](https://www.tandfonline.com/doi/full/10.1080/20964471.2019.1611175)

* [Adoption of machine learning techniques in ecology and earth science](https://oneecosystem.pensoft.net/article/8621/download/pdf/)

* [CIRA Guide To Custom Loss Functions For Neural Networks In Environmental Sciences - Version 1](https://arxiv.org/pdf/2106.09757.pdf)

* [Zero-Shot Learning of Aerosol Optical Properties with Graph NeuralNetworks](https://arxiv.org/pdf/2107.10197.pdf)

* [NeuralHydrology - a collection of papers on using neural networks in hydrology](https://neuralhydrology.github.io/)

* [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?mkt_tok=OTg3LUlHVC01NzIAAAF-KbKDtMdtC6CVBJS0uWL0Paw6uJRdMh8g8FbltivTqUKL3WvP3AdX9MVxc0ySwjknrG7FRo9eqdFeZLkklEjZQkqb-Z2WVUJIUziQdqc)

* [Advancing AI for Earth Science: A Data Systems Perspective](https://eos.org/science-updates/advancing-ai-for-earth-science-a-data-systems-perspective)

* [Google Earth Engine: Planetary-scale geospatial analysis for everyone](https://www.sciencedirect.com/science/article/pii/S0034425717302900)

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## Reports

* [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) 

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## Thoughts

* :sunglasses: :sparkling_heart: [Learning earth system models from observations: machine learning or data assimilation?](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0089)

* [Artificial intelligence: A powerful paradigm for scientific research](https://www.sciencedirect.com/science/article/pii/S2666675821001041)

* [Why 90% of machine learning models never hit the market](https://thenextweb.com/news/why-most-machine-learning-models-never-hit-market-syndication)

* ['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/)

* [37 reasons why your neural network is not working](https://blog.slavv.com/37-reasons-why-your-neural-network-is-not-working-4020854bd607)

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## Competitions

* :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)

* [GPU Hackthons](https://www.gpuhackathons.org/) - designed to help scientists, researchers and developers to accelerate and optimize their applications on GPUs.

* [LANL Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction)

* [HackerEarth](https://www.hackerearth.com/challenges/)

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## Communities

* [ESIP Machine Learning Cluster](https://wiki.esipfed.org/Machine_Learning)

* [ESIP Agriculture and Climate Cluster](https://wiki.esipfed.org/Agriculture_and_Climate)

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## RelatedAwesome

- [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.

- [Awesome-Spatial](https://github.com/RoboDonut/awesome-spatial) – ![Awesome](media/icon/awesome.png) Awesome list for geospatial, not specific to geoscience but significant overlap

- [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

- [Awesome Coastal](https://github.com/chrisleaman/awesome-coastal) – ![Awesome](media/icon/awesome.png) Awesome list for coastal engineers and scientists

- [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

- [Awesome Workflow Engines](https://github.com/meirwah/awesome-workflow-engines) - ![Awesome](media/icon/awesome.png) A curated list of awesome open source workflow engines

- [Awesome Pipeline](https://github.com/pditommaso/awesome-pipeline) - ![Awesome](media/icon/awesome.png) A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin

- [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

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