Integrating AI and spatial analysis for resilient and inclusive 15-minute cities. A case study of Gdańsk

This study explores the application of artificial intelligence (AI) and geospatial tools to design 15-min cities, focusing on proximity, public space quality, environmental resilience, and inclusion. Using the post-industrial Young City district in Gda´ nsk, Poland, as a case study, the research integrates semantic segmentation (DeepLabv3) and AI-driven object detection for evaluating public space quality, flood simulation modeling (InVEST UFRM) for climate resilience, and network analysis using adjusted walking speeds (2.95 km/h) to capture the needs of vulnerable populations. By identifying “hotspots” with compounded deficits in access to urban amenities, public space quality, and resilience, the study provides a replicable, data-informed tool for guiding equitable urban transformation. The findings reveal critical spatial and environmental disparities, particularly in former fence-line neighborhoods in Gda´ nsk, which remain underserved despite ongoing urban redevelopment. The proposed framework offers practical insights for mainstreaming inclusive adaptation strategies into planning policies and contributes to the evolving discourse on operationalizing the 15-min city (FMC) concept in complex contexts.