Land Use/Land Cover Mapping with Google Earth Engine

Classifying land cover across a large region by hand - digitizing boundaries, manually labeling parcels - simply doesn't scale. We built a Land Use/Land Cover (LULC) mapping pipeline on Google Earth Engine that applies machine learning classification directly to satellite imagery, producing consistent, regularly-updatable land cover maps across areas too large to review manually.

The pipeline pulls multi-spectral satellite imagery, applies preprocessing (cloud masking, atmospheric correction, index calculation for vegetation and water), and trains a classifier against labeled reference points to categorize land into classes - forest, cropland, urban, water, bare soil. Because it runs on Earth Engine's infrastructure, re-running the classification on updated imagery is a matter of re-triggering the pipeline, not repeating months of manual digitization.

The output feeds directly into the same PostGIS-backed systems used for other environmental monitoring work, so land-cover change over time becomes just another queryable layer rather than a one-off report.

We built an ML-based classification pipeline on Google Earth Engine that ingests multi-spectral satellite imagery, applies preprocessing and spectral index calculations, and classifies land cover against a trained reference model - producing a full-region map in a fraction of the time manual digitization required, and repeatably enough to track change over successive seasons.