Automatic Land Use Land Cover classification — K-means clustering labeled with NDVI/NDWI/NDBI — refined with your own samples if you want. No training-data pipeline required to get a real result.
Choose how you want to classify this image
What do you want delivered?
How Hybrid Classification Works
Automatic Baseline
K-means clustering groups pixels by spectral similarity, then each cluster gets a real name (Water, Vegetation, Built-up, Bare Soil) from its actual NDVI/NDWI/NDBI values — not guessed, computed.
Refine or Extend
Add your own samples to strengthen an automatic class, or define an entirely new one — both use the exact same underlying signature method.
Outlier Protection
Every new sample is checked against the automatic baseline — if it looks like it belongs to a different class, you get a warning before it's used, not a silently wrong result.
Clean Vector Output
Tiny, isolated misclassified pixels are filtered out before vectorizing — real features survive, noise doesn't become a pile of tiny polygons.
Frequently Asked Questions
What does "Hybrid" mean here?
Every class — whether automatically generated or built from your own clicked samples — is represented the same way, as a spectral signature. The automatic path is genuinely unsupervised (no training data); refining a class or adding your own is genuinely supervised. This tool supports both, and you choose how much of each to use.
What image bands do I need?
Red, Green, and Near-Infrared (NIR) at minimum, for the NDVI/NDWI calculations this tool relies on. A SWIR band (common in Landsat-style 6-band imagery) enables a real built-up/bare-soil distinction via NDBI — without it, that distinction falls back to a coarser brightness-based estimate.
How many sample clicks does adding a class take?
6 initial samples minimum, then up to 6 spatially-spread candidate pixels per verification round to quickly confirm or reject — never a long list, and always optional after the first round.
How accurate is this?
Genuinely useful for a fast, real classification — but this is unsupervised classification refined by your samples, not survey-grade without real ground-truthing. Two spectrally similar but semantically different surfaces can still be confused.
What outputs can I export?
Choose any combination of a classified raster (GeoTIFF), labeled vector polygons, and a branded PDF report with category-wise area — delivered together in one download.
Need a validated, ground-truthed LULC classification for a real project? Our team can build that with proper accuracy assessment.
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