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Yi Zhang

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Proceedings Article | 26 June 2023 Paper

A suggestion method for urban perception improvement using street-view images

Jinhao Sun, Yi Zhang, Xi Yang

Proceedings Volume 12721, 127211K (2023) https://doi.org/10.1117/12.2683558

KEYWORDS: Object detection, Education and training, Safety, Feature extraction, Image segmentation, Design and modelling, Visualization, Roads, Analytical research, Neck

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The evaluation of street view perception plays a crucial role in the design and improvement of urban street layouts by urban planners. However, the most commonly used method for evaluating street views by planners and researchers is through questionnaires, which may not effectively capture users' psychological feelings. Currently, there is a lack of research on providing suggestions for improving street views that score low in perception evaluation. To address this issue, we propose a method for highlighting areas where street views are insufficient in six aspects which are beautiful, safety, wealthy, lively, boring and depressing to provide suggestions for improvement. Reference pictures and corresponding improvements suggested by the proposed system are necessary as they can provide urban planners with intuitive suggestions for street improvement. In this study, we recruit volunteers to rate street view maps in the six aspects mentioned above. We then use the scene graph generation method to characterize the relationship between objects in the street view images. Finally, we apply the graph-matching algorithm SimGNN to identify three pictures that are highly similar to the graph structure of the high-score street views as reference images. This approach effectively provides suggestions for street view images with low-score, while also offering an intuitive way to improve street view perception for urban planners. Overall, our proposed method provides a more comprehensive and effective way to evaluate and improve street views, which can contribute to better urban planning and design.

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