Article in Review
This article is currently in the Reviewing phase. It is undergoing peer review and editorial evaluation.
Abstract
Traditional master planning often relies on decadal, static documents that fail to account for the rapid spatio-temporal dynamics of modern urban environments. This divergence between planned and actual land use creates systemic inefficiencies in urban governance. This research proposes a GeoAI-driven framework—a formal systems approach that integrates Geomatics, Artificial Intelligence (AI), and Deep Learning (DL) into a live, updatable "City Engine." The framework utilises Convolutional Neural Networks (CNN) for automated change detection and GIS-based heuristic rules for instant plan versioning. By shifting the master plan from a static atlas to a dynamic "body of knowledge," the proposed system enables real-time monitoring, evaluation, and publishing of urban development strategies. The results demonstrate that such a system can significantly bridge the implementation gap, offering a scalable model for smart city governance and sustainable regional development.
