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Assessing and forecasting collective urban heat exposure with smart city digital twins
Open accessAbstract
Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.
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“In this sense, GIS- and remote-sensing-based frameworks are often used to combine thermal indicators with environmental and demographic layers to cause urban heat vulnerability or risk maps [5, 6].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [5]
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Cited by
Sorted by citations
GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435
“In this sense, GIS-and remote-sensing-based frameworks are often used to combine thermal indicators with environmental and demographic layers to cause urban heat vulnerability or risk maps [5, 6].”
Metadata
- Authors
- Pan, X., Mavrokapnidis, D., Ly, H. T., Mohammadi, N., & Taylor, J. E.
- Affiliations
- Georgia Institute of Technology (US)
- University College London (GB)
- Corresponding author
- Taylor, John E.
- Venue
- Scientific Reports
- Published
- 2024-04-26
- Volume
- 14
- Issue
- 1
- Pages
- 9653-9653
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11053083
- OpenAlex
- W4395676953
- Cited by
- 33
- References
- 5
- Citations by year
- 2026: 17 · 2025: 11 · 2024: 5
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- SSRN Electronic Journal (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1837021)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Keywords
- urban heat island, crowd simulation, weather sensors, computer vision, public safety, Columbus, Georgia
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