Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991–2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Rapid urbanization has resulted in increased environmental challenges, compounding worries about deteriorating air quality and rising temperatures. As cities become hubs of human activity, understanding the complex interplay of numerous environmental elements is critical for developing effective mitigation solutions. Recognizing this urgency, a framework to highlight the hotspots with critical environmental issues emerges as a comprehensive approach that incorporates key criteria such as the surface urban heat island intensity (SUHII), heat index (HI) and air quality index (AQI) to assess and address the complex web of environmental stressors that grip urban landscapes. Employing the multicriteria decision analysis approach, the proposed framework, named the environmental risk hotspot mapping framework (ERHMF), innovatively applies the analytic hierarchy process at a sub-criteria level, considering long-term heat island trends with recent fluctuations in the HI and AQI. Climate change impact has been symbolized through rising temperatures, as reflected by surface urban heat island intensity trends over two decades. The robustness and correctness of the weights have been assessed by computing the consistency ratio, which came out as 0.046, 0.065 and 0.044 for the sub-criteria of the SUHII, AQI and HI, respectively. Furthermore, the framework delves into the nexus between environmental stressors and vegetation cover, elucidating the role of green spaces in mitigating urban environmental risks. Augmented by spatial and demographic data, the ERHMF adeptly discerns high-risk areas where environmental stress converges with urban development, vulnerable population concentrations and critical vegetation status, thereby facilitating targeted risk management interventions. The framework’s effectiveness has been demonstrated in a regional case study in Italy, underscoring its ability to pinpoint risk hotspots and inform specific policy interventions. The quantitative study undertaken at the sub-administrative level revealed that approximately 6,000,000 m² of land in Bologna are classified as being under high to extremely high environmental stress, with over 4,000,000 m² lying only within the extremely high stress group (90–100). Similarly, 1,000,000 m² of land in Piacenza and Modena have high levels of environmental stress (80–90). In conclusion, the ERHMF presents a holistic methodology for delineating high-risk urban hotspots, providing essential insights for policymakers, urban planners and stakeholders, with the potential to enhance overall urban resilience and foster sustainable development efforts.

Understanding the spatiotemporal dynamics of urban surface temperatures is essential for climate-resilient green planning under intensifying heat stress and rapid land-use change. This study investigates seasonal thermal behavior at a microscale analytical resolution (200 × 200 m) across Bologna, a densely urbanized city with diverse morphology. Satellite-derived land surface temperature (LST) was statistically downscaled to 10 m resolution and subsequently aggregated to the 200 m grid, and integrated with urban morphological and socioeconomic variables, including built surface, tree cover, population density, land-use classes, and settlement zones. A twofold approach was developed: (1) unsupervised k-means clustering to identify thermally distinct clusters per season, revealing consistent spatial patterns where tree-dominated areas remain cooler and dense urban cores warmer; and (2) an explainable deep learning model based on a convolutional neural network (CNN) to quantify variable contributions using permutation-based feature importance, benchmarked against Support Vector Machine, Random Forest, and XGBoost models. Model performance differences were evaluated using paired statistical tests with significance assessed at accepted thresholds. The CNN demonstrated competitive performance, effectively capturing nonlinear relationships in the data and, when combined with subsequent analysis, providing insights into feature relevance. Built surface was the most influential driver (>37% across all seasons), while tree cover (7%-11%) and population density (5%-35%) showed pronounced seasonal variability, reflecting shifts in vegetation activity and energy demand. This integrative, interpretable modeling framework establishes a foundational step toward a neighborhood-scale urban climate digital twin, capable of linking multi-source data to simulate and explain intra-annual heat dynamics. The findings provide critical insights for adaptive, fine-scale heat mitigation and climate-resilient urban green planning.