{"name": "GEE LULC Toolkit", "package_name": "gee_lulc_toolkit", "description": "A single-window QGIS workflow for LULC mapping with Google Earth Engine: load composites and spectral indices, create stratified sample points via 4-view labeling (custom schemes or ESA WorldCover), apply classifiers (RF/GBT/CART/SVM) or unsupervised clustering, and output Olofsson area\u2011adjusted accuracy with 95% CI, Jeffries\u2013Matusita separability, plus an automated map layout builder.", "about": "An end-to-end land-use/land-cover (LULC) classification workflow powered by Google Earth Engine inside QGIS. One combined window guides you from image loading to a finished map: composites and spectral indices, stratified sample points, point labeling (LULC, ESA WorldCover, or custom schemes) with a 4-view labeling window, Random Forest / GBT / CART / SVM or unsupervised clustering, Olofsson area-adjusted accuracy with 95 percent confidence intervals, Jeffries-Matusita separability, and automated map layout builder. Requires the earthengine-api Python package in the QGIS Python environment and a Google Cloud project registered for Earth Engine.", "homepage": "https://github.com/khgeo/gee-lulc-toolkit", "repository": "https://github.com/khgeo/gee-lulc-toolkit", "tracker": "https://github.com/khgeo/gee-lulc-toolkit/issues", "author": "YAM Sarath", "tags": ["landsat", "land cover", "accuracy assessment", "classification", "remote sensing", "sentinel", "random forest", "google earth engine", "lulc", "earth engine"], "downloads": 52, "latest_version": "1.0.7", "versions": [{"version": "1.0.7", "experimental": false, "qgis_min": "3.22.0", "qgis_max": "3.99.0", "downloads": 51, "uploaded_by": "khgeo", "upload_datetime": "2026-07-21T02:38:43.041215"}]}