Tree Growth Workbench
Explainable AI for diagnosing urban-tree growth suitability through spatial validation, diagnosis maps and cell-level Shapley interpretation.
TreeSuit XAI is an explainable-AI research workbench for diagnosing urban-tree growth suitability. It combines spatial-block model comparison, locked-test validation, diagnosis rasters, reference-matched environmental Shapley waterfalls and two area-planting alternatives. Requires a separately installed scientific Python with numpy, pandas, scipy, scikit-learn, statsmodels, xgboost, shap, matplotlib, rasterio, joblib and Pillow; see the user guide and requirements.txt. No automatic dependency installation or data upload. The bundled model is Helsinki-specific. Tested on Windows with QGIS 3.40.11; Linux/macOS unverified; QGIS 4 not supported. Experimental screening, not planting approval. User-created joblib model packages must be trusted.