Here is a huge list of resources for performing deep learning on satellite and aerial imagery. The resources are updated regularly and could benefit both the research and developer communities.
- HiRoNEx is a Python tool for automatic, fully unsupervised extraction of historical road networks from historical maps. Check it out here.
- Check here for a Python script for creating an animated GIF, given a shapefile with time-annotated vector objects (e.g., building footprints and construction year).
- PyGMT is a Python library for processing geospatial and geophysical data and making publication quality maps and figures. Here is a short course on using PyGMT.
- The spl.js library enables using geospatial operations (buffering, intersecting, selecting by location, etc.) on SQLite databases. Check out these example notebooks by Adam Roberts.
- The QGIS Earth Engine plugin integrates Google Earth Engine and QGIS using EE Python API. The user needs to have an active Google Earth Engine (EE) account to use the plugin. Learn more here.
- Here is a global downscaled climate data product spanning multiple downscaling methods (source code included), alongside an interactive mapping tool for inspecting, exploring, and comparing the data.
- This website provides a comprehensive and interactive catalog of reference benchmark datasets. Check it out here.
- Sen4AgriNet is a harmonized multi-country, multi-temporal benchmark dataset for agricultural earth observation machine learning applications. Learn more here.
- The leaflet R package makes it easy to integrate and control Leaflet maps in R. Here is a cheat sheet guiding you in using the leaflet package.
