Optimizing Urban Green Spaces for Reducing Heat Stress in High-Density Urban Environments Using Data-Driven Approaches
Article Sidebar
Main Article Content
The rapid expansion of urban areas has intensified the Urban Heat Island (UHI) effect, particularly in high-density cities characterized by extensive impervious surfaces and limited green spaces. Rising surface temperatures increase energy consumption, reduce outdoor thermal comfort, and pose serious public health risks during extreme heat events. Urban greening is widely recognized as an effective heat mitigation strategy; however, in densely built environments, indiscriminate or uniform distribution of green spaces often fails to achieve optimal cooling benefits. This study proposes an artificial intelligence–based framework for strategically optimizing urban green space placement to maximize heat reduction while accounting for land-use constraints. The proposed approach integrates multisource remote sensing data, vegetation indices, land surface temperature measurements, and urban morphological indicators with machine learning–based thermal modeling. A Random Forest Regression model is employed to capture the nonlinear relationships between vegetation cover, built-up density, and surface temperature, followed by a spatial optimization process to identify priority locations for greening interventions. Experimental results demonstrate a strong negative relationship between vegetation density and land surface temperature, with optimized greening scenarios achieving temperature reductions of up to 2.6°C, significantly outperforming uniform greening strategies with equivalent green area allocation. The findings highlight that the spatial configuration and targeted placement of green spaces are more influential than total green cover alone. By incorporating explainable AI techniques, the framework also provides interpretable insights into the dominant drivers of urban heat, enhancing transparency for planning applications. Overall, this study offers a data-driven and decision-oriented methodology that can support urban planners and policymakers in designing effective, climate-resilient strategies for mitigating heat stress in high-density urban environments.
Downloads
References
Santamouris, M. (2020). Recent advances in urban heat mitigation technologies. Renewable and Sustainable Energy Reviews, 132, 110026. https://doi.org/10.1016/j.rser.2020.110026
Bowler, D. E., Buyung-Ali, L., Knight, T. M., and Pullin, A. S. (2020). Urban greening to combat heat: A systematic review. Landscape and Urban Planning, 97(3), 147–155. https://doi.org/10.1016/j.landurbplan.2010.05.006
Weng, Q. (2020). Remote sensing of urban heat islands: Methods and applications. Remote Sensing of Environment, 242, 111758. https://doi.org/10.1016/j.rse.2020.111758
Qiao, Z., Liu, L., and Qin, Y. (2021). Urban cooling effects of green spaces: Scale and threshold analysis. Urban Forestry & Urban Greening, 62, 127155. https://doi.org/10.1016/j.ufug.2021.127155
Peng, J., Xie, P., and Liu, Y. (2021). Urban landscape pattern optimization for thermal environment improvement. Science of the Total Environment, 781, 146752. https://doi.org/10.1016/j.scitotenv.2021.146752
Massaro, E., Ratti, C., and Chen, Y. (2023). Spatially optimized urban greening for reducing population exposure to extreme heat. Nature Communications, 14, 2765. https://doi.org/10.1038/s41467-023-38326-0
Tanoori, G., Al-Kindi, S., and Al-Hinai, M. A. (2024). Machine learning-based prediction of land surface temperature for urban heat island mitigation planning. Sustainable Cities and Society, 95, 104615. https://doi.org/10.1016/j.scs.2024.104615
Mansour moghaddam, M., Karimi, P., and Nouri, H. (2024). Urban heat island modeling using advanced machine learning techniques and remote sensing data. Remote Sensing, 16(9), 1–21. https://doi.org/10.3390/rs16091687
Zhou, S., Tong, H., Yang, Y., and Weng, Q. (2025). An explainable machine learning framework for identifying heat mitigation thresholds of urban green infrastructure. Sustainable Cities and Society, 101, 104982. https://doi.org/10.1016/j.scs.2025.104982
Zhao, S., and Weng, Q. (2024). Optimization of green space distribution in high-density urban areas for heat mitigation. Sustainable Cities and Society, 98, 104756. https://doi.org/10.1016/j.scs.2024.104756
Zhang, Y., Ren, S., Liu, X., and Weng, Q. (2025). Optimizing urban green space configurations for enhanced heat island mitigation using geographically weighted machine learning. Sustainable Cities and Society, 103, 105120. https://doi.org/10.1016/j.scs.2025.105120
Lin, N., Li, X., and Zhang, Y. (2023). Explainable artificial intelligence for urban vegetation mapping and thermal impact assessment. Remote Sensing, 15(6), 1–19. https://doi.org/10.3390/rs15061635
An, H., Cai, H., and Weng, Q. (2022). Composition and configuration effects of urban green space on land surface temperature. Remote Sensing, 14(3), 1–18. https://doi.org/10.3390/rs14030633
Zhao, L., Oppenheimer, M., and Zhu, Q. (2020). Interactions between urban heat islands and climate change. Nature Climate Change, 10, 506–514. https://doi.org/10.1038/s41558-020-0730-2
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.