Integrating AI Tools and Drone Photogrammetry for Urban Planning and Analysis: Implications for Sociocultural Dynamics, Sustainability, and Pedagogy

ABSTRACT

This study proposes an integrated interdisciplinary framework that combines AI tools with drone photogrammetry to address technical, theoretical and pedagogical dimensions of urban analysis. It uses high‐precision geospatial data ( n  = 737) to validate urban complexity theories and professional competencies. The methodology involves orthomosaic generation, point clouds and 3D models using photogrammetry techniques. AI core methods, such as machine learning and computer vision, specifically automated feature detection, Structure from Motion (SfM) and Multi‐View Stereo (MVS) algorithms embedded in commercial photogrammetry pipelines, supported spatial analysis, improving data quality and analytical capacity. The results demonstrate that automated semantic segmentation facilitates classification of point clouds for urban modelling. Automated feature‐matching and lighting normalisation achieved a mean reprojection error of 0.102 pixels, yielding a high‐fidelity dataset capable of capturing informal urban morphologies that remain unresolved in standard, publicly available satellite imagery. Beyond technical outcomes, a motivation survey of participating students revealed strong intrinsic and extrinsic engagement, while fostering competencies to address complex urban challenges. The study contributes to a technical workflow for AI‐enhanced spatial reconstruction, empirical validation of urban complexity and fractal theories and organisation, and an exploratory pedagogical study on emerging technologies. Overall, the framework improves spatial precision, supports evidence‐based urban governance and contributes to the UN's SDG goal 11.