39 citations as recorded by crossref.

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3. Spatial and severity-dependent controls of thermal comfort across Türkiye: a SHAP-based decomposition of UTCI drivers D. Yavaşlı https://doi.org/10.1007/s00704-026-06389-3
4. Optimizing urban greening and densification in the context of outdoor heat: Opportunities for AI-supported urban adaptation H. Fünfgeld et al. https://doi.org/10.1016/j.landurbplan.2025.105574
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6. Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area D. Cinquegrana et al. https://doi.org/10.3390/atmos15091119
7. Blue–Green Infrastructure Strategies for Improvement of Outdoor Thermal Comfort in Post-Socialist High-Rise Residential Areas: A Case Study of Niš, Serbia I. Bogdanović Protić et al. https://doi.org/10.3390/su172310876
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9. Digital Approaches for Climate-Responsive Urban Planning: A Human-Centred Review of Microclimate and Outdoor Thermal Comfort M. Mahgoub et al. https://doi.org/10.3390/su18083710
10. Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments W. Gong et al. https://doi.org/10.1177/0739456X251391121
11. Hybrid WRF-ML modeling for characterizing inter- and intra-LCZ microclimate variability: A case study of Shenzhen, China J. Huang et al. https://doi.org/10.1016/j.scs.2025.107099
12. Planning for cooler cities: A multimodal AI framework for hyperlocal spatio-temporal urban heat stress prediction and mitigation S. Yi et al. https://doi.org/10.1016/j.ufug.2025.129101
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17. Is satellite land surface temperature an appropriate proxy for intra-urban variability of daytime heat stress? F. Briegel et al. https://doi.org/10.1016/j.rse.2025.115045
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19. Multisensory Urban Climate Zones (MUCZ): A Framework for Mapping Dynamic Multidomain Human Comfort in Complex Urban Fabrics beyond Urban Morphology C. Grapas et al. https://doi.org/10.1016/j.scs.2025.106673
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21. A multi-stage ROM-enhanced CFD-ML framework for efficient 3D urban wind-field prediction H. Chen et al. https://doi.org/10.1016/j.scs.2026.107531
22. Mean radiant temperature in outdoor urban environments: From radiative physics to data-driven and hybrid modelling frameworks G. Mihalakakou & A. Romeos https://doi.org/10.1016/j.buildenv.2026.114595
23. Linear and POD-based interpolation of simulated urban wind fields with experimental validation C. Ebert et al. https://doi.org/10.1016/j.jweia.2026.106468
24. Application of human-centric digital twins: Predicting outdoor thermal comfort distribution in Singapore using multi-source data and machine learning X. Liu et al. https://doi.org/10.1016/j.uclim.2024.102210
25. A Methodological Approach Using ENVI-Met Simulations and Meteorological Data for Assessing Thermal Stress: The Case of Athens (Greece) I. Koletsis et al. https://doi.org/10.3390/atmos17050522
26. Approach for the vertical wind speed profile implemented in the UTCI basics blocks UTCI applications at the urban pedestrian level H. Lee et al. https://doi.org/10.1007/s00484-024-02835-x
27. Walking the heat: why thermal walks matter for high resolution microclimate mapping B. Gottkehaskamp et al. https://doi.org/10.1088/1742-6596/3140/8/082001
28. Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies V. Milica et al. https://doi.org/10.1515/geo-2025-0878
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33. Revisiting the impact of environmental factors on outdoor public space use: A machine learning analysis of social media data W. Qian et al. https://doi.org/10.1016/j.habitatint.2026.103777
34. An expert-based review of Mean Radiant Temperature across ten research domains at urban and building scales M. Matallah et al. https://doi.org/10.1016/j.hssust.2026.04.003
35. Deep learning enables city-wide climate projections of street-level heat stress F. Briegel et al. https://doi.org/10.1016/j.uclim.2025.102564
36. Toward the Next-Generation of Heat-Health Warning Systems and Action Plans A. Matzarakis & C. Giannaros https://doi.org/10.3390/atmos16080938
37. Vertical variations of low-altitude thermal and wind environment across different 3D urban morphologies in Singapore R. Xu et al. https://doi.org/10.1016/j.buildenv.2026.114344
38. Machine learning predicts pedestrian wind flow from urban morphology and prevailing wind direction J. Lu et al. https://doi.org/10.1088/1748-9326/adc148
39. Towards Universal Thermal Climate Index Prediction via machine learning approaches O. Veisi et al. https://doi.org/10.1016/j.rser.2025.115680