Detection and Mitigation of Urban Heat Island Effect using Vision-Language Models

Abstract

Urban Heat Islands (UHIs) intensify urban temperatures, contributing to environmental degradation and public health concerns. Mitigating these effects is crucial for creating more sustainable and liveable cities. In this paper, we address the problem of detecting UHI-prone areas and generating context-specific mitigation strategies using street-level imagery. We propose HeatScape, an AI-powered framework that combines semantic segmentation and Vision-Language Models (VLMs) to identify heat-retaining surfaces from user-captured images. Unlike traditional approaches reliant solely on satellite data, HeatScape uses ground-level visual inputs to offer fine-grained urban analysis. VLMs then interpret these segmented scenes to recommend urban planning actions for new and existing building projects. Our experiments show a UHI detection accuracy of 94.25% using logistic regression, with VLM-generated suggestions aligning with established urban sustainability practices. HeatScape demonstrates a scalable, low-cost tool for data-driven environmental assessment and urban design.