Machine learning approaches to urban heat Island detection and sustainability analysis

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Chinese explanation / 中文解读

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Original abstract

Urban Heat Islands (UHIs) represent a critical barrier to sustainable urbanism and ecological stability in rapidly expanding metropolitan regions. A significant gap exists in operationalising these data for governance and climate-resilient planning, even though remote sensing of heat is a well-established approach. This study proposes a comprehensive conceptual framework for leveraging Machine Learning (ML) to bridge the divide between high-resolution thermal detection and sustainability-focused urban policy. The paper outlines a methodological roadmap for integrating large-scale geospatial datasets, including Land Surface Temperature (LST) and urban morphology into predictive ML architectures such as Random Forest and Convolutional Neural Networks (CNNs), with the synthesis of key interdisciplinary literature. The research moves beyond descriptive analysis to explore the governance-oriented insights of AI, specifically in prioritising green infrastructure expansion and ecological corridor conservation for urban biodiversity. A critical examination of Explainable AI (XAI) is provided to address the trade-offs between model accuracy and the transparency required for public accountability in urban design. This work presents AI-driven UHI mapping not merely as a technical exercise, but as a strategic catalyst for achieving Sustainable Development Goal 11 (SDG 11) to make cities and human settlements inclusive, safe, resilient, and sustainableThe key findings supports a paradigm shift toward interdisciplinary, AI-informed urban management to preserve the ecological resilience against the dual pressures of climate change and accelerated urbanisation.

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