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<p>Managing the rapid urbanization is a major challenge for governments worldwide in the 21st century, with an estimated 6 billion people expected to live in urban areas by 2050. Rapid urbanization intensifies the heat island (HI) effect, making cities hotter than nearby rural areas due to anthropogenic activities, dense infrastructure, and limited green spaces. HI exacerbates public health issues, strains infrastructure, degrades air quality and water availability, and increases energy consumption, all of which impair urban resilience and sustainability. Efficient mitigation of HI effects requires accurate mapping and monitoring. This review presents a systematic framework following PRISMA guidelines, synthesizing 120 peer-reviewed articles from 2000 to 2025 to map and analyse surface heat island (SHI) and atmospheric heat island (AHI). The study evaluates high-resolution satellite imagery datasets integrated with geographic information systems (GIS) and advanced machine learning techniques for land surface temperature (LST) retrieval, spatiotemporal analysis, prediction, and heat vulnerability assessment. The synthesis highlights that integrating thermal infrared satellite imagery with spectral indices and machine learning enhances the accuracy and predictive performance of heat island spatiotemporal mapping. Additionally, Geostationary and polar satellites provide complementary spatiotemporal monitoring capabilities, while hybrid deep learning models enhance temperature prediction and vulnerability assessment. The review consolidates insights on HI formation, classification, spatiotemporal characteristics, Impacts, and mitigation strategies. The review provides valuable insights for heat-resilient and sustainable smart city planning and development.</p>

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Remote sensing and machine learning approaches for heat island mapping and analysis: a review and framework for sustainable smart cities

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Venkata Sudhakar Chowdam,
  • Venkata Naresh M,
  • Ganjikunta Ganesh Kumar,
  • Suresh Babu Potladurty

摘要

Managing the rapid urbanization is a major challenge for governments worldwide in the 21st century, with an estimated 6 billion people expected to live in urban areas by 2050. Rapid urbanization intensifies the heat island (HI) effect, making cities hotter than nearby rural areas due to anthropogenic activities, dense infrastructure, and limited green spaces. HI exacerbates public health issues, strains infrastructure, degrades air quality and water availability, and increases energy consumption, all of which impair urban resilience and sustainability. Efficient mitigation of HI effects requires accurate mapping and monitoring. This review presents a systematic framework following PRISMA guidelines, synthesizing 120 peer-reviewed articles from 2000 to 2025 to map and analyse surface heat island (SHI) and atmospheric heat island (AHI). The study evaluates high-resolution satellite imagery datasets integrated with geographic information systems (GIS) and advanced machine learning techniques for land surface temperature (LST) retrieval, spatiotemporal analysis, prediction, and heat vulnerability assessment. The synthesis highlights that integrating thermal infrared satellite imagery with spectral indices and machine learning enhances the accuracy and predictive performance of heat island spatiotemporal mapping. Additionally, Geostationary and polar satellites provide complementary spatiotemporal monitoring capabilities, while hybrid deep learning models enhance temperature prediction and vulnerability assessment. The review consolidates insights on HI formation, classification, spatiotemporal characteristics, Impacts, and mitigation strategies. The review provides valuable insights for heat-resilient and sustainable smart city planning and development.