Geospatial Neural Networks: Enhancing Smart City Through Location Intelligence

Large cities are increasingly leveraging technology to enhance urban living and operational efficiency. The emergence of Geospatial Neural Networks (GeoNNs) offers a novel solution to improve smart city initiatives through advanced location intelligence. By effectively integrating both spatial and temporal data, GeoNNs can learn from the complexities of urban environments. This innovative method enhances applications in areas such as traffic management, environmental monitoring, and urban planning. Utilizing graph neural networks, our approach captures the intricate interactions among urban elements, which significantly aids in informed decision-making. GeoNNs also support real-time data processing, essential for responding dynamically to the demands of smart city infrastructure.

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Published
2025-03-21
DOI
https://doi.org/10.1109/icsi64877.2025.11009922
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.01
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article

Geospatial Neural Networks: Enhancing Smart City Through Location Intelligence

Junjie Chen
1 citations
Traffic Prediction and Management Techniques
1.01
article

Geospatial Neural Networks: Enhancing Smart City Through Location Intelligence

Junjie Chen
article en
1 citations

Abstract

Large cities are increasingly leveraging technology to enhance urban living and operational efficiency. The emergence of Geospatial Neural Networks (GeoNNs) offers a novel solution to improve smart city initiatives through advanced location intelligence. By effectively integrating both spatial and temporal data, GeoNNs can learn from the complexities of urban environments. This innovative method enhances applications in areas such as traffic management, environmental monitoring, and urban planning. Utilizing graph neural networks, our approach captures the intricate interactions among urban elements, which significantly aids in informed decision-making. GeoNNs also support real-time data processing, essential for responding dynamically to the demands of smart city infrastructure.

Columbia University (US)
Sustainable cities and communities
Openalex Percentile: Top 25%
Traffic Prediction and Management Techniques
1.01
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