What does this research mean for the field?

Integrating machine learning architectures with high-resolution geospatial data provides a strategic, governance-oriented framework for mitigating Urban Heat Islands and advancing sustainable urban planning. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

The aim is to develop a framework using machine learning for detecting urban heat islands and promoting sustainable urban policies.

June 6, 2026Open Access

Machine learning approaches to urban heat Island detection and sustainability analysis

Key Points

The aim is to develop a framework using machine learning for detecting urban heat islands and promoting sustainable urban policies.
Utilized high-resolution thermal detection data and ML methods including Random Forest and CNNs.
Integrated large-scale geospatial datasets like Land Surface Temperature (LST) and urban morphology.
Examined the role of Explainable AI to balance model accuracy and transparency for governance.
Demonstrated improved accuracy in UHI detection using machine learning models compared to traditional methods.
Identified key areas for green infrastructure expansion and ecological corridor conservation.
Provided insights into AI's governance implications for enhancing urban biodiversity.

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.

Bookmark

View Full Paper