{"name": "Tree Growth Workbench", "package_name": "tree_growth_waterfall", "description": "Explainable AI for diagnosing urban-tree growth suitability through spatial validation, diagnosis maps and cell-level Shapley interpretation.", "about": "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.", "homepage": "https://github.com/sleepyheadzzzzzz/Tree-Point-Cloud-Training-and-Analysing", "repository": "https://github.com/sleepyheadzzzzzz/Tree-Point-Cloud-Training-and-Analysing", "tracker": "https://github.com/sleepyheadzzzzzz/Tree-Point-Cloud-Training-and-Analysing/issues", "author": "Yao Chaowen", "tags": ["trees", "carbon", "suitability", "spatial validation", "shap", "planting", "explainable ai"], "downloads": 14, "latest_version": "2.1.2", "versions": [{"version": "2.1.2", "experimental": true, "qgis_min": "3.40.0", "qgis_max": "3.99.0", "downloads": 14, "uploaded_by": "sleepyheadzzzzzz", "upload_datetime": "2026-09-05T08:27:32.114853"}]}