Earth Observations Using Python (2nd Edition) - Datasets and Machine Learning Models

Introduction This repository contains datasets and models for use with the book Earth Observations Using Python (Second Edition). data.zip This zip file contains freely available satellite data for use. The data support the hands-on examples and exercises throughout the book, primarilly in netCDF4 format but also includes HDF, text, and GRIB2. All data originate from publicly available sources and are redistributed here for convenience, so readers can get started without navigating agency data portals. NOAA: NOAA CLASS OSPO Products PSL Catalog GML FTP Website NASA: Earthdata Portal AERONET Portal EUMETSAT: Catalog JMA: AWS Opendata Registry models.zip This compressed file contains machine learning models (PyTorch, ResNet) for use in Chapter 12 of the book. Models are provided as saved weights (.pt files) and their accuracy and precision (.csv files). They were trained to perform land classification taskes using the public available EuroSAT dataset. The goal is to illustrate how to perform machine learning research and are not optimized for operational use, so please use at your own risk for other purposes. License and AttributionThe data files in this repository belong to their respective agencies (e.g., NOAA, NASA, EUMETSAT, JMA) and are subject to each agency's terms of use. No data have been modified. The curation and selection of examples is the work of the author, except where otherwise noted. Please credit the author by citing the book if you use these examples in your own work.