This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in rapidly urbanizing cities, particularly in developing countries. Using Bangkok, Thailand, as a case study, this research presents a problem-driven approach that leverages open geospatial data, including Overture Maps and OpenStreetMap (OSM), alongside Digital Elevation Models, to overcome limitations in data availability, granularity, and quality. This study integrates morphological terrain analysis and machine learning-based classification models to estimate building ancillary attributes such as footprint, height, and usage, applying micro-dasymetric mapping techniques to refine population distribution estimates. The findings reveal a notable degree of accuracy within residential zones, whereas performance in commercial and cultural areas indicates room for improvement. Challenges identified in mixed-use and townhouse building types are attributed to issues of misclassification and constraints in input data. The research underscores the importance of geospatial AI and remote sensing in resolving urban data scarcity challenges. By addressing critical gaps in geospatial data acquisition and processing, this study provides scalable, cost-effective solutions in the integration of multi-source remote sensing data and machine learning that contribute to sustainable urban development, disaster resilience, and resource planning. The findings reinforce the transformative role of open-access geospatial data in Earth observation applications, supporting real-time decision-making and enhanced urban resilience strategies in rapidly evolving environments.

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

Understanding urban diversity is critical to inclusive planning for sustainable urban development. This study introduces a new Urban Diversity Index (UDI) based on global point-of-interest (POI) data for food-related establishments—defined here as facilities that offer food and beverage services, including various kinds of eating and drinking venues —covering 249 cities across 154 countries. The UDI integrates three components: Pielou’s Evenness Index (J′) to capture the balance of establishment types, a Coverage Ratio (C′) to measure global representativeness of establishment categories, and density (ρ′) to reflect spatial concentration. By applying concentric buffer analysis around city centers, we evaluate the spatial profiles of diversity in each city. Results show that while cities like London and Istanbul have similar index components, they exhibit significant differences in the spatial extent and pattern of high-diversity zones, reflecting their unique morphological and regulatory contexts. Furthermore, the analysis of “Peak Distance Buffer Zones”—areas where UDI remains above 95% of its maximum—reveals diverse urban forms, particularly in Asian megacities. Scatterplots of standardized UDI and peak distances identify distinct typologies of urban diversity structures. Notably, urban population size showed no significant correlation with UDI values. Overall, this study demonstrates the utility of globally standardized POI-based metrics in capturing the spatial heterogeneity of urban qualitative diversity and offers new insights into cross-city comparisons of urban complexity.