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Here is a hands-on coding lesson teaching geospatial concepts and Python packages for querying, accessing and processing geospatial data.

The Gym is a toolbox to segment imagery with a variety of a family of UNet models, facilitating fully reproducible label-to-model workflows.

Here is a free open access book including fundamentals and applications of Google Earth Engine. It suits users of all levels - from beginners to advanced users.

This blog post introduces image segmentation and clarifies the different types of segmentation (semantic, instance and panoptic) and discusses annotation for segmentation projects.

Magrit is an intentionally simple application designed for teaching and learning cartography. It lets you import your own geospatial datasets and render and combine a wide variety of maps.

This blog post describes a method to utilize Google Earth Engine from within BigQuery's SQL allowing SQL speakers to get access to and value from data available within Earth Engine.

Here are some tools for working with Google Earth Engine from a Jupyter development environment which provide a foundation for new libraries on top of the Google Earth Engine Python API.

This repository contains the code and configuration files to reproduce semantic segmentation results of Swin Transformer.

CloudSEN12 is a large dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches that are evenly spread throughout all continents except Antarctica.

This notebook demonstrates how to import different geographic data formats (e.g., GeoJSON, TopoJSON, Shapefile, GeoPackage and KML/KMZ) into Observable using various libraries.