Tutorial: Remote Sensing Image Classification with QGIS

In this tutorial, you walked through the complete workflow for preparing and classifying remote‑sensing imagery in QGIS. You created a clean working environment, prepared ground‑truth data, segmented parcels using the AI Segmentation plugin, and applied the Semi‑Automatic Classification Plugin to build and evaluate a Random Forest model. Together, these steps showed how traditional GIS methods and modern AI‑assisted tools can complement each other to produce accurate, reproducible land‑cover maps.

By now, you should feel confident in setting up a classification project, organizing training data, running supervised classification, and assessing the quality of your results. These skills form the foundation for more advanced remote‑sensing analyses, whether you are mapping crop types, monitoring land‑use change, or supporting water‑productivity assessments.