GeoClassify

A literature-based raster classification toolkit for QGIS

GeoClassify is a free, open-source QGIS 4 plugin that automates the reclassification of raster datasets using scientifically validated, peer-reviewed threshold values. It integrates reclassification, automatic styling, CRS-aware area calculation, and batch processing into a single workflow — eliminating the need for manual threshold entry, symbol design, or separate area computation.

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Features

• 14 built-in classification schemes — vegetation indices, topographic parameters, climate variables, land use/land cover, and erosion risk, all with literature-derived thresholds
• Interactive preview editor — inspect and modify any classification range, label, or colour before processing
• User-defined classifications — create, save, edit, import, and export custom schemes in JSON format
• CRS-aware area calculator — geodetically correct per-class areas (m², km², %) for both projected and geographic (WGS 84) coordinate systems
• Automatic QML style application — classified layers are styled and added to the QGIS project immediately
• Batch processing — reclassify an entire folder or a selected list of raster files in a single run
• CSV export — per-class area statistics saved alongside every output raster

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Supported Classification Schemes

Category	Types
Vegetation indices	NDVI, NDWI, SAVI, EVI, NDMI, NBR, BSI
Topographic data	Elevation, Slope, Aspect
Climate data	Annual mean temperature, Annual total precipitation
Land use / land cover	LULC (8 classes)
Risk analysis	Erosion risk class
Custom	Unlimited user-defined schemes (JSON)

All default thresholds are traceable to peer-reviewed publications (Tucker, 1979; Huete, 1988; Gao, 1996; Key & Benson, 2006; and others — see references).

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Requirements

Component	Version
QGIS	4.0 or later (Qt6 / PyQt6)
Python	3.9+ (bundled with QGIS)
GDAL	3.0+ (bundled with QGIS)
NumPy	Any recent version (bundled with QGIS)

No additional external Python packages are required.

QGIS 3.x users: Replace PyQt6 import statements with PyQt5 equivalents — a two-line change documented in __init__.py. All functionality is otherwise identical on QGIS 3.22 LTR or later.

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Installation

Option 1 — QGIS Plugin Manager (recommended)

1. Open QGIS.
2. Go to Plugins → Manage and Install Plugins.
3. Search for GeoClassify.
4. Click Install Plugin.

Option 2 — Manual installation from ZIP

Windows

1. Extract the GeoClassify folder from the ZIP archive.
2. Copy the folder to:
   %APPDATA%\QGIS\QGIS3\profiles\default\python\plugins\
3. Restart QGIS.
4. Enable the plugin in Plugins → Manage and Install Plugins → Installed.

Linux

cp -r GeoClassify ~/.local/share/QGIS/QGIS3/profiles/default/python/plugins/

macOS

cp -r GeoClassify ~/Library/Application\ Support/QGIS/QGIS3/profiles/default/python/plugins/

Alternatively, use the provided installation scripts:

# Linux / macOS
chmod +x install.sh && ./install.sh

# Windows — double-click install.bat

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Quick Start

1. Load a raster layer in QGIS (e.g. a pre-computed NDVI raster).
2. Go to Raster → GeoClassify.
3. Select the input layer and classification type.
4. Set an output file path.
5. Click OK — a Classification Preview dialog opens.
6. Review or adjust the thresholds, then click Continue.
7. The classified layer is added to the map with automatic styling.

For a full step-by-step tutorial see the wiki.

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Computing Indices Before Classification

GeoClassify classifies pre-computed rasters. Use the QGIS Raster Calculator to derive indices from multi-band imagery before running the plugin.

Index	Expression (Sentinel-2)	Expression (Landsat 8/9)
NDVI	(B8 - B4) / (B8 + B4)	(B5 - B4) / (B5 + B4)
NDWI	(B3 - B8) / (B3 + B8)	(B3 - B5) / (B3 + B5)
SAVI	((B8 - B4) / (B8 + B4 + 0.5)) * 1.5	((B5 - B4) / (B5 + B4 + 0.5)) * 1.5
EVI	2.5 * (B8 - B4) / (B8 + 6*B4 - 7.5*B2 + 1)	2.5 * (B5 - B4) / (B5 + 6*B4 - 7.5*B2 + 1)
NDMI	(B8 - B11) / (B8 + B11)	(B5 - B6) / (B5 + B6)
NBR	(B8 - B12) / (B8 + B12)	(B5 - B7) / (B5 + B7)
BSI	((B11 + B4) - (B8 + B2)) / ((B11 + B4) + (B8 + B2))	((B6 + B4) - (B5 + B2)) / ((B6 + B4) + (B5 + B2))

For topographic parameters (slope, aspect), use Raster → Analysis → Slope / Aspect in QGIS before classifying.

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User-Defined Classifications

Custom schemes are stored in custom_classifications.json. You can create them interactively through the plugin UI, or author them directly in JSON:

{
  "my_lst": {
    "name": "Land Surface Temperature",
    "description": "Urban thermal zones from Landsat TIRS Band 10.",
    "unit": "°C",
    "ranges": [
      { "min": 10, "max": 20, "label": "Cool surface",      "color": "#87CEEB" },
      { "min": 20, "max": 30, "label": "Moderate surface",  "color": "#FFFF00" },
      { "min": 30, "max": 40, "label": "Warm surface",      "color": "#FF8C00" },
      { "min": 40, "max": 60, "label": "Urban heat island", "color": "#FF0000" }
    ]
  }
}

Export and import schemes via GeoClassify → Manage Classifications → Export / Import JSON to share them across projects or with collaborators.

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Supported File Formats

Type	Formats
Input rasters	GeoTIFF (.tif, .tiff), ERDAS Imagine (.img), ASCII Grid (.asc)
Output raster	GeoTIFF (.tif)
Style file	QGIS Layer Style (.qml)
Area statistics	CSV (.csv)
Custom schemes	JSON (.json)

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Contributing

Contributions are welcome — especially new classification schemes backed by peer-reviewed literature.

# 1. Fork the repository
# 2. Create a feature branch
git checkout -b feature/new-scheme

# 3. Add your scheme to classification_library.py following the existing pattern
# 4. Commit and push
git commit -am "Add <scheme name> classification"
git push origin feature/new-scheme

# 5. Open a Pull Request

When submitting a new built-in classification scheme, please include the full citation for the threshold values in your PR description.

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References

The default classification thresholds are derived from the following peer-reviewed publications:

• Rouse, J. W., et al. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, NASA.
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
• Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
• Liu, H. Q., & Huete, A. (1995). A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457–465.
• Gao, B. C. (1996). NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
• Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385–396.
• Key, C. H., & Benson, N. C. (2006). Landscape assessment (LA): Sampling and analysis methods. In FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service.
• Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43(1), 39–47.
• Renard, K. G., et al. (1997). Predicting Soil Erosion by Water: A Guide to Conservation Planning with RUSLE. USDA Agricultural Handbook No. 703.
• Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47–56.

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Citation

If you use GeoClassify in your research, please cite:

Örücü, Ö. K. (2025). GeoClassify: A literature-based raster classification toolkit for QGIS (Version 1.0.0) [Software]. Süleyman Demirel University. https://github.com/omerorucu/GeoClassify

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License

This project is licensed under the MIT License — see the LICENSE file for details.

Copyright © 2025 Ömer K. Örücü, Süleyman Demirel University, Isparta, Türkiye.