Land use / land cover (LULC) classification answers one question: what's actually covering the ground in this image, pixel by pixel? Done by eye, it's slow and subjective — two people looking at the same scene will draw the boundaries differently. Done with plain unsupervised clustering, it's fast but incomplete: a clustering algorithm will happily group pixels by spectral similarity, but it has no idea which cluster is "water" and which is "vegetation." Someone still has to look at cluster #3 and decide what it actually represents.
The part clustering alone doesn't do
K-means and similar unsupervised methods are genuinely good at their actual job: grouping pixels that look spectrally alike. What they don't do is attach real-world meaning to a group. "Cluster 3" and "Cluster 7" mean nothing on their own — someone has to inspect each one and manually assign a label, and that step is where consistency (and a lot of time) usually gets lost, especially across a large scene with dozens of clusters.
Automatic baseline, or add your own samples
The automatic path is genuinely unsupervised — no training data required, ready as soon as your imagery is uploaded. If a class needs strengthening, or you need one the automatic baseline didn't capture, you can add your own samples on top of it. Both the automatic and the sample-based classes use the exact same underlying spectral signature method, so refining doesn't mean switching to a different system — it's the same approach, with more input.
What imagery you need
Red, Green, and Near-Infrared (NIR) bands at minimum — that's what the NDVI and NDWI calculations behind the automatic labeling actually rely on. A SWIR band, common in Landsat-style 6-band imagery, adds a real built-up/bare-soil distinction via NDBI; without it, that specific distinction falls back to a coarser brightness-based estimate rather than disappearing entirely.
How to do it
-
Upload your imagery
A multi-band GeoTIFF works, with Red, Green, and NIR bands at minimum. Add a SWIR band too, if your imagery has one, for the sharper built-up/bare-soil distinction.
-
Choose automatic or refine
Run the automatic baseline on its own, or add your own samples to strengthen a class or define a new one — both use the same underlying method.
-
Build your classes
If refining, click a handful of sample points to start a class, then quickly confirm or reject a few spatially-spread candidate pixels per round — optional after the first pass.
-
Export your outputs
Choose any combination of a classified raster, labeled vector polygons, and a branded PDF report with category-wise area — all in one download.
Ready to classify your own imagery?
Open Hybrid LULC Classification →