🛰️ GIS Classification
Land cover classification using machine learning
1. Upload Data
Raster File (GeoTIFF)
Select Landsat or similar multispectral imagery
Shapefile Files (select all: .shp, .shx, .dbf, .prj)
Hold Ctrl/Cmd to select multiple files. Required: .shp, .shx, .dbf
2. Configure
Classification Strategy
Random Forest (Recommended)
Support Vector Machine
Logistic Regression
Maximum Likelihood (MLE)
Class Column
Estimators (Random Forest)
SVM Kernel
Linear (Fast)
RBF (Accurate but slow)
Max Depth (Random Forest, optional)
Leave empty for automatic depth
C Parameter (SVM, Logistic Regression)
Regularization strength (default: 1.0)
Training Parameters
Validation Split
Fraction of data for validation (default: 0.2)
Random Seed
For reproducibility (default: 42)
3. Train classifier
Train Model
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Accuracy
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Cohen's Kappa
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Train Samples
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Validation Samples
Classes:
4. Classify and visualize
Load Template
-- Select Template --
Pre-defined class names and colors
Save Template
Export
Import
Edit Class Names & Colors
Apply to Map
Layer Opacity:
70%
Adjust classification layer transparency
Classify Raster
Download Result
Classes