Globally, urbanization is accelerating, with China witnessing a significant 40% rise in urbanization rate over the past 4 decades. However, the dynamic changes in the spatial coupling between infrastructure and utilization intensity during the early, middle, and late stages of urbanization are not clear. The trajectory of development and coupling within the urbanization process is crucial for understanding issues such as urban over-saturation and urban shrinkage. Using Hegang in Northeastern China as an example, we utilized high-resolution remote sensing data, examined the construction intensity of urban land use, analyzed the degree of coupling with utilization efficiency, and clarified the dynamic evolution of the binary relationship system between development and coupling. Results show that Hegang’s construction intensity has seen a continuous rise from 1992 to 2000, with a 200.06% increase over 28 years, while its coupling with utilization efficiency has experienced a significant drop in the 21st century, suggesting a persistent decline in the utilization of buildings and a notable urban shrinkage phenomenon. Considering development status and coupling degree, we delineate a characteristic urbanization state curve for Hegang, reflecting its progression through stages of “Underdeveloped, Highly coupled,” to “Underdeveloped, Weakly coupled”, and finally to “Highly developed, Weakly coupled”, offering insights into its urban development path. This research not only establishes a foundational data groundwork for future land-use planning in Hegang but also presents a replicable template for urbanization path analysis in other cities, contributing to a broader understanding of urban development dynamics.

Urban heat and extreme-temperature risks increasingly constrain sustainable planning in semi-arid grassland towns. Taking Xi Ujimqin Banner as a case study, this research develops a remote-sensing-informed diagnosis-to-allocation framework for heat-resilient spatial planning. The framework first uses remote-sensing-derived land surface temperature (LST) and multi-source spatial indicators to identify model-explained associations between LST and ecological, morphological, land-use, accessibility, and socioeconomic variables. A CatBoost model with SHAP, PDP, and ICE interpretation was applied to explain dominant LST-related patterns. Spatial units were then classified by integrating LST intensity, sky view factor (SVF)-related spatial openness, land-use structure, ecological sensitivity, and economic output characteristics. Finally, a Quasi-oppositional Learning and Levy-flight enhanced Cheetah Optimization Algorithm (QLA-COA)-based constrained optimization model was used to generate alternative allocation schemes. In the framework, LST reduction and land-development economic benefit were treated as the two primary planning objectives, while SVF-related openness was incorporated as an auxiliary spatial form and radiative geometry indicator rather than as a direct temperature-control objective. Results show that CatBoost achieved the best LST prediction performance (R² = 0.834, RMSE = 1.762 °C, MAE = 1.371 °C). Cooling-priority, openness-priority, economic-priority, balanced-development, and ecological-restricted units accounted for 18.7%, 15.8%, 12.9%, 26.9%, and 25.7% of all units, respectively. The balanced-development scheme reduced mean LST by 1.30 °C while maintaining SVF-related spatial openness and improving economic benefits. This study provides a practical decision-support framework for climate-adaptive spatial governance in semi-arid grassland regions by linking LST diagnosis, spatial response translation, ecological constraints, auxiliary openness evaluation, and allocation-scheme comparison. The source code and processed spatial unit dataset used for model training, SHAP interpretation, and PDP/ICE visualization are publicly available.