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Resources for deep learning with satellite & aerial imagery
This repository will guide you how to use machine learning algorithms for land use land cover classification using Sentinel-2 data.
Search and download Landsat scenes from EarthExplorer.
Authoring Books and Technical Documents with R Markdown
CAVAanalytics is a comprehensive framework for climate data analysis, offering streamlined access to data, advanced processing and visualization capabilities. It is designed to support a wide range…
This repository provides inference code to compute canopy height maps from aerial images, as described in the paper "Very high resolution canopy height maps from RGB imagery using self-supervised v…
All the files mentioned in the article on Towards Data Science Neural Network for Landsat Classification Using Tensorflow in Python | A step-by-step guide.
Classification of Satellite Images Using Google Earth Engine JS API for Data Exporting and TensorFlow
EuroSAT: Land Use and Land Cover Classification with Sentinel-2
interactive notebooks from Planet Engineering
FAIR Chemistry's library of machine learning methods for chemistry
Download and process satellite imagery in Python using Sentinel Hub services.
A network-based python package to understand and model urban complexity
A list of STAC endpoints for the AWS Open Data Program
Demo app exploring the US power grid & renewable plants - Built with Dash, Dask, Datashader & Coiled
Paired down version of OSL images to run locally
An advanced geospatial data analysis platform
Links and slides from the OpenGeoHub summer school 2023