Check out this four-part series demonstrating how to use machine learning for detecting changes in land cover. Open source libraries and tools are used for this tutorial.
Land-cover-classification
- Here is an Google Earth Engine guided project on land cover analysis. These video tutorials with open-source code show how to work with land cover data in Google Earth Engine.
- Radiant Earth has released LandCoverNet, which will enable the creation of high-resolution and up-to-date maps for natural resource management.
- 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.
- 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.
- This article explains how to train a segmentation model for classifying the use of cropland, based on a land cover dataset for South America.
- Read here about PEARL - on open sourced platform that allows you to do fast AI-based land classification without writing a line of code.
