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Cited by 59Urban Heat Island MitigationClimate Change and Health ImpactsLand Use and Ecosystem Services

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A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights

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Li, Fei, Yigitcanlar, Tan, Nepal, Madhav, Thanh, Kien Nguyen, Dur, Fatih·2024·Remote Sensing
DOI 10.3390/rs16163032
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Abstract

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing the vulnerability of urban residents to extreme heat. Although many studies have assessed urban heat vulnerability, there is a significant lack of standardized criteria and references for selecting indicators, building models, and validating those models. Many existing approaches do not adequately meet urban planning needs due to insufficient spatial resolution, temporal coverage, and accuracy. To address this gap, this paper introduces the U-HEAT framework, a conceptual model for analyzing urban heat vulnerability. The primary objective is to outline the theoretical foundations and potential applications of U-HEAT, emphasizing its conceptual nature. This framework integrates machine learning (ML) with remote sensing (RS) to identify urban heat vulnerability at both long-term and detailed levels. It combines retrospective and forward-looking mapping for continuous monitoring and assessment, providing essential data for developing comprehensive strategies. With its active learning capacity, U-HEAT enables model refinement and the evaluation of policy impacts. The framework presented in this paper offers a standardized and sustainable approach, aiming to enhance practical analysis tools. It highlights the importance of interdisciplinary research in bolstering urban resilience and stresses the need for sustainable urban ecosystems capable of addressing the complex challenges posed by climate change and increased urban heat. This study provides valuable insights for researchers, urban administrators, and planners to effectively combat urban heat challenges.

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Figure 1 ML and RS integration framework for urban sustainability [47]. Figure 1. MLand RS integration framework for urban sustainability [47].
Figure 1 ML and RS integration framework for urban sustainability [47]. Figure 1. MLand RS integration framework for urban sustainability [47].CC-BY · From the PDF
Figure 2 Framework of urban heat vulnerability analysis with ML and RS. Figure 2. Framework of urban heat vulnerability analysis with ML and RS.
Figure 2 Framework of urban heat vulnerability analysis with ML and RS. Figure 2. Framework of urban heat vulnerability analysis with ML and RS.CC-BY · From the PDF
Figure 3 Likely differences of mapping urban heat vulnerability in census tracts and grid scale. Figure 3. Likely differences of mapping urban heat vulnerability in census tracts and grid scale.
Figure 3 Likely differences of mapping urban heat vulnerability in census tracts and grid scale. Figure 3. Likely differences of mapping urban heat vulnerability in census tracts and grid scale.CC-BY · From the PDF
Figure 4 Flowchart of U-HEAT implementation. Figure 4. Flowchart of U-HEAT implementation.
Figure 4 Flowchart of U-HEAT implementation. Figure 4. Flowchart of U-HEAT implementation.CC-BY · From the PDF
Figure 5 Integrated vulnerability and risk indicator framework. Figure 5. Integrated vulnerability and risk indicator framework.
Figure 5 Integrated vulnerability and risk indicator framework. Figure 5. Integrated vulnerability and risk indicator framework.CC-BY · From the PDF
Figure 6 Visualization of a hypothetical urban heat vulnerability mapping. Figure 6. Visualization of a hypothetical urban heat vulnerability mapping.
Figure 6 Visualization of a hypothetical urban heat vulnerability mapping. Figure 6. Visualization of a hypothetical urban heat vulnerability mapping.CC-BY · From the PDF

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Li, F., Yigitcanlar, T., Nepal, M., Thanh, K. N., & Dur, F.
Affiliations
  • Queensland University of Technology (AU)
  • City 4.0 Lab, School of Architecture and Built Environment, Faculty of Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
  • School of Electrical Engineering and Robotics, Faculty of Engineering, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2024-08-18
Volume
16
Issue
16
Pages
3032
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs16163032
OpenAlex
W4401703721
Cited by
59
References
6
Citations by year
2026: 28 · 2025: 27 · 2024: 4
FWCI
6.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
Keywords
urban heat vulnerability, urban heat island, machine learning, urban resilience, climate change adaptation, urban planning, heat risk mapping, active learning (machine learning)

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This page
CoChat. (2026). Citation check: A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights. https://app.cochat.ai/papers/10.3390/rs16163032
The paper
Li, F., Yigitcanlar, T., Nepal, M., Thanh, K. N., & Dur, F. (2024). A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights. Remote Sensing. https://doi.org/10.3390/rs16163032