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Updated: Jul 2, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Enhancing Urban Data Analysis: Leveraging Graph-Based Convolutional Neural Networks for a Visual Semantic Decision

Nikolaos Sideris¹, Georgios Bardis¹, Athanasios Voulodimos²

¹Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

Convolutional Neural Networks (CNNs) combined with graph-based urban data show improved performance for urban planning decisions. This approach enhances the selection of optimal locations for services and infrastructure, outperforming previous random forest methods.

Keywords:

convolutional neural networks decision support graph visualization machine learning urban planning

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Area of Science:

Urban Planning
Data Science
Computer Vision

Background:

Modern urban environments generate vast amounts of data from diverse sensors, creating challenges in data integration, visualization, and utilization.
Effective urban planning requires optimal site selection for commercial activities, public utilities, and infrastructure reuse, necessitating advanced data analysis techniques.

Purpose of the Study:

To evaluate the effectiveness of Convolutional Neural Networks (CNNs) with graph-based urban data representation for urban planning decision support.
To compare the performance of CNNs against previous methods, specifically random forests, using a consistent dataset.

Main Methods:

Utilized a graph-based representation for urban data, leveraging its inherent visual characteristics.
Employed Convolutional Neural Networks (CNNs) for classification tasks, inspired by their image-based data processing capabilities.
Compared CNN performance against random forests on a standardized urban dataset for location selection.

Main Results:

The CNN approach demonstrated improved performance across several key indices compared to the random forest baseline.
The combination of CNNs and graph-based urban data proved effective for decision support in urban planning.
Results indicate a promising potential for this methodology in addressing complex urban planning challenges.

Conclusions:

CNNs integrated with graph-based urban data offer a superior approach for urban planning decision support.
This method enhances the ability to select suitable locations for urban development and infrastructure.
The findings suggest a significant advancement in utilizing complex urban data for informed planning.