Work

Automated Local Climate Zone Mapping via Multi-Parameter Synergistic Optimization and High-Resolution GIS-RS Fusion

Year: 2025

Type: article

Abstract: Local Climate Zone (LCZ) classification is essential for urban microclimate modeling and heat mitigation planning. Traditional methods relying on manual sampling face limitations in scalability, objectivity, and handling spatial heterogeneity. This study presents an automated framework for LCZ sample generation, facilitating efficient large-scale LCZ mapping and LCZ-based urban climate analysis and geospatial applications. To this aim, it proposes a dual-path automated framework integrating GIS-driven sample generation to enhance LCZ classification accuracy: a multi-parameter Synergistic Optimization approach for urban LCZs and a Distance-driven Maximum Coverage method for natural LCZs. Specifically, urban samples are selected via multi-objective optimization and Pareto front screening for quality and representativeness, while the selection of natural samples prioritizes spatial coverage and diversity. Combining urban morphological parameters with Sentinel-2 imagery and a Random Forest classifier yielded a final accuracy of 0.95 in our test site, confirming the framework’s effectiveness.

Source: Remote Sensing

DOI: 10.3390/rs17122038

Authors: Wenbo Li, Ximing Liu, Alim Samat, Paolo Gamba

Institutions: University of Pavia, Al-Farabi Kazakh National University, Chinese Academy of Sciences, Xinjiang Institute of Ecology and Geography

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $3,258