Object detection-based evaluation of the basic service facilities in urban community regeneration

Purpose Evaluating basic service facilities is crucial for revitalizing aging urban communities. These communities often face limitations due to scarce resources and labor-intensive assessment processes. Traditional manual methods not only take up considerable time but also lack consistency. This study explores the use of computer vision to create a scalable and objective approach for assessing basic service facilities. Design/methodology/approach A workflow-based methodology is proposed, incorporating a visual-feature system aligned with community typologies. The approach targets three core scenarios: external elevator retrofitting, parking resource assessment and sanitary condition monitoring. Using community-level imagery and object detection algorithms, the system automates visual feature extraction. Findings The study introduces the Community Basic Service Index (CBSI) as a composite indicator for evaluating the basic service facilities in old residential communities. Results demonstrate that the automated method is more efficient and consistent than manual inspection, especially in processing speed, reducing labor and standardizing data, while still providing similar reliability in most evaluation tasks. Originality/value This research focuses on the community level, highlighting the physical and operational conditions of basic service facilities that directly impact residents' daily lives. It creates a data-driven method to measure facility conditions and regeneration success. By applying computer vision techniques to assess community-level basic service facilities, the study offers a new methodological approach for an objective, efficient, and comparable evaluation in urban community regeneration.