The Impact of Tree Data on Urban Heat Island Mapping: A San José Case Study

Publication Date

1-1-2025

Document Type

Conference Proceeding

Publication Title

Proceedings 2025 IEEE Conference on Artificial Intelligence Cai 2025

DOI

10.1109/CAI64502.2025.00061

First Page

336

Last Page

341

Abstract

Urban areas experience the Urban Heat Island (UHI) effect, with higher temperatures than rural areas, disproportionately impacting low-income communities. Mapping UHIs is a process that usually requires significant amount of human resources, and is not scalable. The lack of accurate and detailed UHI maps makes it difficult for decision makers to design effective mitigation strategies. In this work we introduce a cost-effective, and universally applicable UHI mapping approach using open-source data and Artificial Intelligence (AI) to extract features from remote sensing imagery. Using various causative factors such as city characteristics, anthropogenic heat, city canyons, and meteorological variables, we create a UHI map for the city of San José, California, focusing on the effect different approaches on greenery detection have in the resulting UHI map. Our findings indicate that tree data is more effective for analyzing the UHI effect in urban areas, while general vegetation is better for rural areas. This work demonstrates AI's potential to enhance UHI mapping.

Keywords

deep learning, GIS, machine learning, tree canopy, UHI Mapping, Urban Heat Islands, urban planning

Department

Computer Engineering

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