Urban Thermal Archetype Classification in the Context of Urban Development Transformation Using Machine Learning Techniques
In pursuit of urban climate resilience, it is crucial to characterize the spatial heterogeneity of urban thermal environments for spatially targeted mitigation, with the local climate zone (LCZ) framework emerging as a prevailing and powerful tool. However, the inherent complexity and highly dynamic...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11103724/ |
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| Summary: | In pursuit of urban climate resilience, it is crucial to characterize the spatial heterogeneity of urban thermal environments for spatially targeted mitigation, with the local climate zone (LCZ) framework emerging as a prevailing and powerful tool. However, the inherent complexity and highly dynamic nature of urban-atmosphere interactions necessitate complementary approaches to observe and interpret subtle (e.g., intra-LCZ) built-environment alterations and associated thermal responses, particularly in contexts such as urban renewal. This study collected 21 indicators of urban composition, urban form, urban function, and socio-economy to provide a more complete and detailed portrayal of real-world built environments in Wuhan. Then, five machine learning approaches, including <italic>k-means</italic>, agglomerative hierarchical clustering, Gaussian mixture model, density-based spatial clustering of applications with noise, and autoencoder combined with <italic>k-means</italic> (AE+<italic>k-means</italic>) were used to recognize urban thermal archetypes in large volumes of geospatial data. The clustering results were evaluated using both internal and external metrics, with urban thermal performance presented using reliable and representative summer land surface temperature (LST) data reconstructed through rigorous data selection and spatio-temporal image fusion. Results revealed the robust <italic>k-means</italic> to be optimal. The derived urban thermal archetypes demonstrated improved spatial coherence and enhanced interpretation of LST variations, with an increase in <italic>R</italic>² from 0.31 to 0.48 as compared to LCZ. Sankey diagram further certified the superiority of our approach in capturing LST heterogeneity, particularly in high-temperature areas. Then, geographically weighted regression was applied to reveal the spatially varying relationships between urban indicators and LST. This integrated analysis provided valuable insights into localized drivers of urban heat, supporting the development of targeted and practical climate-adaptive planning strategies tailored to specific urban contexts, particularly in the context of urban renewal. |
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| ISSN: | 1939-1404 2151-1535 |