Lite Classify

Object-based land-cover mapping of drone orthomosaics, offline on one Windows workstation.

Lite Classify splits an RGB orthomosaic into objects, describes each one by its color, texture, shape, height and appearance, helps you find the classes the scene actually contains, learns those classes from examples you click, and checks the finished map against an independent, design-based sample.

• User manual: https://leobarua.github.io/liteclassify/manual/
• Download: two offline Windows installers, from the latest release (mirror on Google Drive)
  • CPU (about 320 MB): for any PC
  • GPU (about 1.8 GB): for PCs with an NVIDIA GPU, which run segmentation and DINOv2 faster

How it works

You work through four tabs, left to right:

1. Load, View & Segment: load the orthomosaic and an optional DSM, color-correct, resample, and segment into objects with SLIC (CPU or GPU, tiled for large scenes). Each object gets 24 features: color, vegetation indices, GLCM texture, shape, elevation, slope, height above ground, and contrast with its neighbors.
2. Discover Classes: each object's surroundings are described by a frozen DINOv2 ViT-S/14 (or, optionally, a DINOv3 ViT-S+/16 weights file you obtain from Meta) at 5 m and 10 m. The objects are over-clustered with k-means, and you review, merge, split and name the groups to build the class list and the first training samples.
3. Classification: label more samples by clicking, dragging or growing a selection, with help from "Find Similar Segments" and "What to Label Next". Check class separability (Jeffries–Matusita), train a Random Forest, retrain until the map stops improving, and export a GeoTIFF with a QGIS style. An optional pixel-level greenness refinement recovers grass strips narrower than the objects. A large image can be processed one extent at a time.
4. Accuracy Assessment: draw a stratified or simple random sample, label the points by eye or import field data, and get the error matrix plus area-weighted accuracy and area estimates with confidence intervals (Olofsson et al., 2014; Stehman, 2014).

Running from source

Python 3.13 on Windows 11 is the tested setup.

python -m venv .venv
.venv\Scripts\pip install -r requirements.txt        # PyTorch with CUDA 12.4
.venv\Scripts\python liteclassify.py

On a machine without an NVIDIA GPU, use requirements-cpu.txt instead. The DINOv2 weights (about 85 MB) are downloaded once on first use, or read from models/ if you place them there.

To build the executables and installers, see installer/README.md.

Repository layout

Path	Contents
liteclassify.py	Launcher: splash screen, then the main window; --selftest for headless checks
obia_app.py	The Qt application: the four tabs and their workflow
obia_engine.py	Raster I/O, segmentation, features, classification support, accuracy assessment (no Qt)
obia_discover.py	DINOv2 context embeddings and the Discover grouping
obia_ui.py	Reusable widgets: map viewer, toggle switch, collapsible sections, separability figure
app_info.py	Name, version and branding
build_exe.py, installer/	PyInstaller build and the Inno Setup installers
docs/manual/	The user manual (HTML) and the scripts that generate its figures
experiments/cnn/	The offline experiments behind the DINOv2 appearance features
third_party/dinov2/	Vendored DINOv2 backbone code (Apache 2.0)

License

Lite Classify is free software under the GNU General Public License v3, with the additional attribution terms in NOTICE.md. Modified versions must keep the credit line below, be marked as modified, and use a different name:

Based on Lite Classify by L. D. Barua (LeoBar) — https://github.com/leobarua/liteclassify

For other licensing terms, see COMMERCIAL-LICENSING.md. To contribute, see CONTRIBUTING.md; contributions require agreeing to the CLA.

Citing

If you publish work made with Lite Classify, please cite the version you used:

Barua, L. D. (2026). Lite Classify (Version 1.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.23877519

To cite all versions, use the concept DOI 10.5281/zenodo.23462352. Other formats (BibTeX, APA) are under "Cite this repository" on GitHub, generated from CITATION.cff.