Study of urban heat island regulation as a configuration problem

Surface urban heat island (SUHI) mitigation policies are traditionally based on quantity‑based indicators such as vegetation cover (NDVI) and built‑up density. However, these indicators systematically fail to explain SUHI intensity across nine geographically distributed study areas spanning (six continents, seven climate zones, and eleven years of MODIS data). The least urbanized study area (Khyber Pakhtunkhwa, Pakistan) records the strongest SUHI (+ 3.48 °C), while a compact megacity (Tianjin, China) with 9.8× higher built‑up density records a moderately high SUHI (1.82 °C). To diagnose the missing structural mechanism, this paper introduces an unsupervised deep learning pipeline (SimCLR‑WRN‑DSPViT) that measures spatial configuration directly from remote sensing patches without SUHI labels. The derived silhouette coherence score explains 50% of cross‑study area SUHI variance ( r = − 0.71, p = 0.03), substantially outperforming any conventional indicator. PCA dimensionality, cosine similarity, and UMAP provide complementary characterizations of structural differences among the study areas, while silhouette coherence remains the primary quantitative metric for cross-study-area SUHI variability. These findings shift SUHI mitigation from a quantity problem to a configuration problem: where and how cooling elements are integrated within the urban fabric. The proposed framework provides a scalable, label‑free toolkit for morphology‑sensitive urban heat mitigation.