AI-Enabled Urban Heat Management: Integrating Remote Sensing, Predictive Analytics and Adaptive Infrastructure

Charles Eesley

AI-Enabled Urban Heat Management: Integrating Remote Sensing, Predictive Analytics and Adaptive Infrastructure

Author(s)	Charles Eesley
Country	United States
Abstract	Rapid urbanisation, climate change, land-use transformation, and the increasing concentration of impervious surfaces have intensified urban heat exposure in cities worldwide. Urban heat islands can increase ambient temperatures, energy consumption, thermal discomfort, and risks to vulnerable populations. Artificial intelligence (AI), combined with remote sensing, geographic information systems, Internet of Things (IoT) sensors, and predictive analytics, provides new opportunities for monitoring and managing urban heat dynamically. This paper examines an integrated approach to AI-enabled urban heat management, focusing on the role of satellite remote sensing, machine learning, spatial analytics, predictive modelling, and adaptive infrastructure. The study develops a conceptual framework connecting remotely sensed environmental indicators with meteorological observations, urban morphology, land-use characteristics, demographic vulnerability, and real-time sensor data. The proposed framework enables cities to move from conventional heat mapping toward predictive and adaptive heat management. Applications include urban heat-risk forecasting, heat-vulnerability mapping, cooling infrastructure planning, green-space optimisation, reflective-surface deployment, energy-demand prediction, and targeted public-health interventions. The paper also discusses challenges related to data quality, spatial and temporal resolution, model transferability, explainability, privacy, infrastructure costs, and unequal access to cooling resources. The analysis proposes an AI-enabled Urban Heat Management Framework based on five interconnected stages: sensing, prediction, risk assessment, adaptive intervention, and continuous evaluation. The paper concludes that AI can significantly strengthen urban climate resilience when it is combined with reliable environmental data, locally appropriate infrastructure, community participation, and transparent governance.
Keywords	Ubanr Heat Island, Artificial Intelligence, Remote Sensing, Predictive Analytics, Urban Climate, Adaptive Infrastructure, Machine Learning, Climate Resilience, Smart Cities, Heat Risk.
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-11

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Enabled Urban Heat Management: Integrating Remote Sensing, Predictive Analytics and Adaptive Infrastructure

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