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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
Last synced: about 5 hours ago
JSON representation
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Papers
- Towards practical artificial intelligence in Earth sciences
- A Review of Earth Artificial Intelligence
- Big Earth data analytics: A survey
- Adoption of machine learning techniques in ecology and earth science
- CIRA Guide To Custom Loss Functions For Neural Networks In Environmental Sciences - Version 1
- Zero-Shot Learning of Aerosol Optical Properties with Graph NeuralNetworks
- NeuralHydrology - a collection of papers on using neural networks in hydrology
- Ten Ways to Apply Machine Learning in Earth and Space Sciences
- Advancing AI for Earth Science: A Data Systems Perspective
- Google Earth Engine: Planetary-scale geospatial analysis for everyone
- Adoption of machine learning techniques in ecology and earth science
- Towards practical artificial intelligence in Earth sciences
- Towards practical artificial intelligence in Earth sciences
- Towards practical artificial intelligence in Earth sciences
- Towards practical artificial intelligence in Earth sciences
- A Review of Practical AI for Remote Sensing in Earth Sciences
- Towards practical artificial intelligence in Earth sciences
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Thoughts
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- Learning earth system models from observations: machine learning or data assimilation?
- Artificial intelligence: A powerful paradigm for scientific research
- Why 90% of machine learning models never hit the market
- '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
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
- 37 reasons why your neural network is not working
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Courses
- ICESat-2 Hackweek
- GeoSMART Machine Learning Curriculum
- ML Seminar: Physics-informed Machine learning for weather and climate science
- ML Seminar: Scalable Geospatial Analysis
- Artificial Intelligence for Earth System Science (AI4ESS) Summer School - hackathon-2020) [readinglist](https://www2.cisl.ucar.edu/sites/default/files/AI4ESS%20Webpage%20PDF%20Recommended%20Readings.pdf)
- American Meterological Survey AI Webinar Series
- USGS Artificial Intelligence/Machine Learning Workshop
- Fundamentals of ML and DL in Python - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
- Stanford CS 229 ML Cheatsheets
- RadiantEarth ML4EO Bootcamp 2021
- Summer School on High-Performance and Disruptive Computing in Remote Sensing - Scaling Machine Learning for Remote Sensing using Cloud Computing
- Artificial Intelligence for Earth System Science (AI4ESS) Summer School - hackathon-2020) [readinglist](https://www2.cisl.ucar.edu/sites/default/files/AI4ESS%20Webpage%20PDF%20Recommended%20Readings.pdf)
- Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School - 30, 2021.
- Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School - 30, 2021.
- Artificial Intelligence for Earth System Science (AI4ESS) Summer School - hackathon-2020) [readinglist](https://www2.cisl.ucar.edu/sites/default/files/AI4ESS%20Webpage%20PDF%20Recommended%20Readings.pdf)
- USGS Artificial Intelligence/Machine Learning Workshop
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Books
- Artificial Intelligence in Earth Science
- Artificial Intelligence Methods in the Environmental Sciences
- Deep Learning for the Earth Sciences
- How to achieve AI maturity and why it matters? (PDF)
- 70-Years-of-Machine-Learning-in-Geoscience-in-Review
- Artificial Intelligence in Earth Science
- Artificial Intelligence Methods in the Environmental Sciences
- Deep Learning for the Earth Sciences
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Tools
- flashflight: - flashflight: A C++ standalone library for machine learning.
- eo-learn
- EarthML
- ML visualization tool - 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
- mlflow - MLflow: A Machine Learning Lifecycle Platform,
- Snips NLU
- 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 - trained SavedModels that can be reused to solve new tasks with less training time and less training data.
- Polyaxon - Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. A Machine Learning Platform for Kubernetes.
- 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 - 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 - 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 - OneFlow is a performance-centered and open-source deep learning framework.
- ml.js - ml.js - Machine learning tools in JavaScript.
- BentoML - BentoML is an open-source framework for high-performance ML model serving.
- Xarray-Beam - Python library for building Apache Beam pipelines with Xarray datasets.
- pygeoweaver - Python library for AI & geospatial workflow management, FAIRness, tangibility and productivity improvement
- EarthML
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Tutorials
- GeoSMART Machine Learning Curriculum & Use Cases
- NASA Openscapes Earthdata Cloud Cookbook
- Artificial Intelligence in Earth science Book Materials
- RadiantEarth MLhub Tutorials
- Machine Learning Tutorials (general, not Earth science specific)
- Pixel-level land cover classification
- EO-learn-workshop - EO-learn-workshop: Bridging Earth Observation data and Machine Learning in Python,
- Machine Learning for Development
- ELSI-DL-Bootcamp - Intro to Machine Learning and Deep Learning for Earth-Life Sciences,
- UW WaterhackerWeek - Introduction to Machine Learning on Landslide Data and Scikit-learn from [UW WaterhackerWeek](https://waterhackweek.github.io/),
- Planet Snow Mapping - Introduction to using Planet imagery to map snow cover
- Machine Learning Pipeline for Climate Science - an end-to-end pipeline for the creation, intercomparison and evaluation of machine learning methods in climate science
- 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
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Training Data
- Kaggle Earth Science Training Dataset
- Radiant MLHub - an open library for geospatial training data
- Google Earth Engine Data Catalog
- EuroSAT Dataset - EuroSAT Dataset: Land Use and Land Cover Classification with Sentinel-2,
- STanford EArthquake Dataset (STEAD) - A Global Data Set of Seismic Signals for AI
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Communities
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Code
- Image Classification Neural Network Ranking with source code - paperswithcode has put together a list of cutting-edge papers and ranked them with the claimed accuracy.
- Earth System Emulator (ESEm) - A tool for emulating geophysical datasets including (but not limited to) Earth System Models
- EmissionAI - Microsoft AI for Earth Project: AI Monitoring Coal-fired Power Plant Emission from Space
- BassNet - preprint](https://arxiv.org/abs/1612.00144) - Deep Learning for Land-cover Classification in Hyperspectral Images,
- Landsat Time Series Analysis for Multi-Temporal Land Cover Classification
- EarthEngine-Deep-Learning - Deep Learning on Google Earth Engine,
- 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.
- EQTransformer - An AI-Based Earthquake Signal Detector and Phase Picker.
- Tropical Cyclone Windspeed Estimator - Winning solutions for Tropical Cyclone Wind Speed Prediction Competition
- Object-based Classification on Earth Engine - Object-based land cover classification with Feature Extraction and Feature Selection for Google Earth Engine (GEE),
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Videos
- Tutorial on Microsoft Azure Machine Learning Studio (AutoML-Regression) - fired Power Plant Emission from Space.
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Reports
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Competitions
- 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.
- LANL Earthquake Prediction
- HackerEarth
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RelatedAwesome
- Awesome Machine Learning - ![Awesome](media/icon/awesome.png) A curated list of awesome Machine Learning frameworks, libraries and software
- Awesome-Open-Geoscience
- Awesome-Spatial
- Awesome Open Climate Science
- Awesome Coastal
- Awesome Workflow Engines - ![Awesome](media/icon/awesome.png) A curated list of awesome open source workflow engines
- Awesome Pipeline - ![Awesome](media/icon/awesome.png) A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
- Awesome Satellite Imagery Datasets - ![Awesome](media/icon/awesome.png) List of aerial and satellite imagery datasets with annotations for computer vision and deep learning
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