Awesome-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, what is awesome?, and Code of Conduct.

Contents

ML-enthusiastic Earth Scientific Questions

Courses

• 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.

• Artificial Intelligence for Earth System Science (AI4ESS) Summer School repo readinglist

• American Meterological Survey AI Webinar Series

• USGS Artificial Intelligence/Machine Learning Workshop

Books

• How to achieve AI maturity and why it matters? (PDF)

• 70-Years-of-Machine-Learning-in-Geoscience-in-Review

Tools

• eo-learn: Earth observation processing framework for machine learning in Python,

• EarthML website: Tools for working with machine learning in earth science,

• 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 is a research framework for fast prototyping of reinforcement learning algorithms,

• mlflow - MLflow: A Machine Learning Lifecycle Platform,

• Snips NLU Snips NLU (Natural Language Understanding) is a Python library that allows to extract structured information from sentences written in natural language.

• 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 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 - Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. A Machine Learning Platform for Kubernetes.

• MMLSpark - MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. 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.

• flashflight: - flashflight: A C++ standalone library for machine learning.

Tutorials

• Pixel-level land cover classification

• EO-learn-workshop - EO-learn-workshop: Bridging Earth Observation data and Machine Learning in Python,

• Machine Learning for Development Machine Learning for Development: A method to Learn and Identify Earth Features from Satellite Images,

• 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,

• Planet Snow Mapping - Introduction to using Planet imagery to map snow cover,

Training Data

• Kaggle Earth Science Training Dataset

• Radiant MLHub

• Google Earth Engine Data Catalog

• University of California Irvine Machine Learning Repository

• EuroSAT Dataset - EuroSAT Dataset: Land Use and Land Cover Classification with Sentinel-2,

• Awesome Satellite Imagery Datasets - Awesome Satellite Imagery Datasets: A curated list of deep learning training datasets,

• STanford EArthquake Dataset (STEAD)

Code

• BassNet,paper-preprint - Deep Learning for Land-cover Classification in Hyperspectral Images,

• MTLCC - Multitemporal Land Cover Classification Network (ConvLSTM, ConvGRU),

• Landsat Time Series Analysis for Multi-Temporal Land Cover Classification

• EarthEngine-Deep-Learning - Deep Learning on Google Earth Engine,

• Continuous Change Detection and Classification - Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data,

• [Object-based Classification on Earth Engine] - Object-based land cover classification with Feature Extraction and Feature Selection for Google Earth Engine (GEE),

• [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.

Videos

Journals

Reports

• Workshop Report: Advancing Application of Machine Learning Tools for NASA’s Earth Observation Data

Thoughts

• ['Farewell Convolutions' – ML Community Applauds Anonymous ICLR 2021 Paper That Uses Transformers for Image Recognition at Scale]

Competitions

• [HackerEarth]

Newsletters

Communities

• ESIP Machine Learning Cluster

• ESIP Agriculture and Climate Cluster

RelatedAwesome

• Awesome-Open-Geoscience – 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 – Awesome list for geospatial, not specific to geoscience but significant overlap
• Awesome Open Climate Science – Awesome list for atmospheric, ocean, climate, and hydrologic science
• Awesome Coastal – Awesome list for coastal engineers and scientists
• Awesome Satellite Imagery Datasets - List of aerial and satellite imagery datasets with annotations for computer vision and deep learning