A Dynamic Framework for a GeoAI-Driven Updatable Master Planning System
DOI:
https://doi.org/10.34257/LJRCST227900UKKeywords:
Artificial Intelligence, City master plan, Deep Learning, Development strategies, Geomatics, GIS, Planning approaches.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.
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