Archive: Land-cover-mapping

Read here about PEARL - on open sourced platform that allows you to do fast AI-based land classification without writing a line of code.

Here is a step-by-step guide on quantifying land-use within a region using QGIS processing tools and the ESA WorldCover dataset.

This article explains how to train a segmentation model for classifying the use of cropland, based on a land cover dataset for South America.

Check out this workflow demo for generating cropland maps with machine learning and CropHarvest, a global dataset for crop-type classification. Link to end-to-end workflow resources included.

Dynamic World provides global, near real-time land cover data at a 10 meter resolution, giving an unprecedented level of detail about what's on the land and how it's being used.

This project maps tree extent at the ten-meter scale using open source artificial intelligence and satellite imagery. Learn more here.

The European Space Agency has released the WorldCover 2021 map based on Sentinel-1 and Sentinel-2 constellations with 11 classes, 10m resolution and 77% accuracy independently validated.