Investigating the application of geographic artificial intelligence (GeoAI) in urban management and planning
Subject Areas :
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Keywords: Geographic Artificial Intelligence (GeoAI), Urban Management, Urban Planning, Spatial Data, Machine Learning, Smart Cities, GIS,
Abstract :
This paper provides a comprehensive examination of the applications of Geographic Artificial Intelligence (GeoAI) in urban management and planning. With the rapid growth of spatial data and an increasing demand for precise analyses in urban processes, GeoAI—through the integration of machine learning algorithms, advanced analytical techniques, and three-dimensional visualization—plays a pivotal role in identifying complex patterns and forecasting trends in urban growth and land-use changes. In this study, the foundational concepts and theoretical framework of GeoAI are first delineated, followed by an investigation of its practical applications in enhancing transportation systems, reducing traffic congestion, identifying high-density areas, and managing crises in large metropolitan environments.
The utilization of GeoAI, by merging data derived from remote sensing, Internet of Things (IoT) sensors, and digital mapping technologies, empowers urban managers to access accurate and up-to-date information, thereby facilitating strategic decision-making aligned with sustainable development goals. By simultaneously analyzing spatial and attribute data, this technology effectively uncovers the strengths and weaknesses of existing urban systems and offers optimal solutions for planning and resource management. Moreover, the deployment of GeoAI contributes to reduced operational costs, enhanced efficiency in resource allocation, and overall improvement in the quality of urban services.
The findings of this study indicate that employing Geographic Artificial Intelligence as a transformative tool in urban planning can significantly contribute to the development of smarter, more dynamic, and sustainable cities. In addition to presenting a robust conceptual framework, this paper offers recommendations for further advancement of related technologies and their integration into urban management systems, thereby charting new horizons for improved urban performance.
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