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FETCH (Framework for Environmental Type Classification Hub) is a QGIS-based tool for automated Local Climate Zone (LCZ) classification. It combines Google Solar API data acquisition with advanced geospatial processing to analyze urban morphology and climate characteristics.
Multi-source data fusion for urban heat mitigation modelling across six European cities. Combines satellite imagery, 3D morphology, and street-level data with explainable ML (XGBoost + SHAP).
This study identifies the driving factors influencing summer air temperature and develops a high-resolution temperature map using regression kriging in Seoul.
Reference implementation of the ML pipeline from Alinasab et al. (2025), Int J Biometeorol 69:1645-1662 - Bayesian-optimized classifiers (KNN/DT/SVM/RF/XGBoost/CatBoost) predicting UTCI/PET/PMV from street morphology, with SHAP interpretation
Reference implementation of the GSCC (Global Source-Conditioned Conductance-Circuit) method for mapping relative urban ventilation capacity from building morphology
Code and open dataset for 'Beyond Local Climate Zones: Continuous Morphology Archetypes for Heat Mitigation Across 66 Cities' (Sustainable Cities and Society, 2026). 33,715 harmonised 1 km² patches across 66 cities and 15 Köppen zones.
An AI/ML-Powered Geospatial Decision Support System That Integrates Satellite Imagery, Weather Data, And Urban Morphology To Identify Urban Heat Hotspots, Simulate Cooling Strategies, And Recommend Optimal, Location-Specific Interventions To Reduce Heat And Improve Climate Resilience.