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- The 15-minute city in Porto, Portugal: accessibility for the elderlyPublication . Guerreiro, Maria; Dinis, Maria Alzira Pimenta; Sucena, Sara; Pereira, Madalena Sofia Araujo; Silva, Isabel; Ferreira, Diogo; Silva Moreira, RuiThe concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.
- Automating city accessibility constraints mapping through AI‐assisted scanning of street view imageryPublication . Silva Moreira, Rui; Moita, Sérgio; Torres, José Manuel; Alberto Ribeiro Gouveia, Feliz; Dinis, Maria Alzira Pimenta; Ferreira, Diogo; Pereira, Madalena Sofia Araujo; Guerreiro, Maria João S.; Zhou, GuyueUrban environments often pose challenges for individuals with mobility impairments due to inadequate pedestrian infrastructure. In addition, the lack of accurate mapping of accessibility features limits the ability to monitor and address these constraints effectively. This paper introduces a framework for Automating City Accessibility Mapping using AI (ACAMAI), that is, provides an AI-assisted pipeline for the automated identification and geolocation of urban accessibility constraints using Google Street View (GSV) panoramas. The ACAMAI pipeline comprises two main stages: (i) training a YOLOv8 object detector to recognise accessibility-related features, such as curb ramps, missing ramps, obstacles and surface problems, in 2D sidewalk images; and (ii) scanning 360° GSV panoramas by extracting multiple perspective views to be analysed by the trained model. The model was trained on a combination of international (Project Sidewalk Dataset—PSD) and local (Porto Dataset—PTD) datasets, achieving high performance across classes, including 91% recall and 85% precision for curb ramps. In the panorama scanning stage, using a fine angular iterative step (2°) maximised the recall, reaching 90% for curb ramps and 93% for obstacles in a locally annotated dataset (GSV Panorama Porto Dataset—GSV-PPD). Although this improved detection coverage, it also led to a high number of redundant predictions, which contributed to a reduced overall precision. Finally, identified constraints are georeferenced and mapped onto OpenStreetMap (OSM), supporting scalable and inclusive urban planning.