Residential environments are central to addressing urban heat stress for vulnerable populations and are prime target areas for implementing climate adaptation strategies. The reliance on urban heat island (UHI) intensity mapping alone has been argued to provide limited guidance for adaptation efforts, whereas linking heat patterns to the built environment characteristics through frameworks such as Local Climate Zones (LCZ) provides actionable insights for developing neighborhood cooling strategies. However, the widely used LCZ maps have a few limitations, such as misrepresenting variation within types because they cannot account for sub-classes beyond the standardized framework. This paper presents an unsupervised clustering approach to identify residential typo-morphologies across 99 Dutch cities, enhancing their relevance for urban heat vulnerability assessments. The analysis reveals that five morphological and canopy parameters (FSI, GSI, OSR, Havg, and FVC) selected from 17 parameters are sufficient to identify nine distinct residential typo-morphologies relatable to LCZs within 100 m × 100 m grid cells. The evaluations demonstrate that our approach detects underrepresented LCZ types and reveals new sub-classes absent from standard LCZ classifications. Key findings include detection of high-density areas (LCZ 42) reflecting recent urban densification with one of the highest UHImax next to LCZ 2 (4.2–4.9 K), and vegetation-differentiated variants within sparse and low-rise categories LCZ 9D and LCZ 6D, distinguished by distinctive UHImax (0.5–0.7 K) higher compared to their reference base types. Notably, tree coverage remains low across low-rise and compact typo-morphologies, revealing substantial opportunities for greening interventions. This data-driven refinement preserves LCZ's global comparability while considering local specificity, providing improved frameworks to inform targeted climate adaptation strategies in residential environments.

Addressing high-temperature exposure in cities requires understanding multiple environmental dimensions, with urban morphology playing a central role. Urban morphology—which include building density, height, and arrangement—significantly influences microclimatic conditions and opportunities for adaptation. A widely used framework for studying these relationships is the Local Climate Zones (LCZ), which provides a solid theoretical foundation for understanding urban climate variations. However, LCZ categories are often idealized and may not accurately reflect the complexity of real-world environments, particularly when attempting to describe both morphological and thermal properties simultaneously.

Although urban form and thermal behavior are inherently interrelated, similar urban forms can exhibit different thermal responses depending on factors like vegetation cover, impervious surfaces, and building materials. To better represent real-world variability, separating morphological classifications from thermal characteristics allows for an analysis that accounts for these differences.

To address these challenges, we develope an approach that generates empirically derived urban morhophological types while maintaining connections to LCZ categories. Our tool systematically classifies urban morphological types for fine-grained, nationwide assessments, enabling consistent comparisons across diverse Dutch urban residential areas. This approach uses readily available geospatial data and applies unsupervised machine learning techniques to identify urban morphological typologies. By standardizing the classification process into 100 x 100 m grid cells from Statistics Netherlands, our method provides a consistent spatial and temporal framework that transcends changing administrative boundaries.

Our approach helps streamline vulnerability analysis by facilitating the intersection of multiple environmental and social dimensions. We demonstrate the tool's utility through an explorative analysis that identifies which socio-economic groups reside in neighborhoods with high heat exposure, considering both morphological types and additional factors influencing heat exposure. This tool provides urban planners and researchers with an empirically-grounded framework for identifying priority areas in existing settlements for scalable adaptation interventions across different urban contexts.

The urban heat island effect is increasingly affecting the quality of life in cities, and detailed data is crucial in designing mitigation policies. However, weather stations are predominantly situated outside urban environments, limiting their ability to represent the varying air temperatures within street canyons. This data paper addresses this limitation by presenting a dataset of the modeled daily maximum urban heat island (UHImax) effect across 99 Dutch municipalities during the summer of 2023. This is achieved by implementing a semi-empirical equation that incorporates readily available meteorological variables and two key urban morphological indicators, namely the sky view factor and fractional vegetation cover. Two primary datasets are presented: (1) a high-resolution dataset of modeled UHImax, and (2) a sky view factor dataset. Both datasets are provided in GeoTIFF format at a 5-meter spatial resolution. Additionally, this paper presents a straightforward methodology for obtaining UHImax values for other periods. The datasets and accompanying methodology provide valuable resources for advancing urban climate research, urban planning and heat mitigation strategies in the Netherlands.