Liu, Xinyi;
Haworth, James;
Wang, Meihui;
(2023)
A New Approach to Assessing Perceived Walkability.
In:
GeoIndustry '23: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications.
(pp. pp. 16-21).
ACM
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Abstract
Walkability is becoming increasingly important in urban planning, public health, and environmental protection. Traditional assessment tools like streetscape images and semantic segmentation focus on objective factors, while questionnaires as the main tool for perceived walkability are limited by cost and scale. This study introduces a new method using the Multimodal Contrastive Learning Model, CLIP, to assess perceived walkability by analysing both tangible and subjective factors such as safety and attractiveness. The method compares perceived with physical walkability by scoring street view images with a customized scale. Initial results indicate CLIP can identify pedestrian-friendly streetscapes that might score low on physical metrics. While its accuracy needs more evaluation, CLIP offers a cost-effective alternative without needing extensive labelled datasets. This method can be combined with objective pedestrian assessment methods to serve as reference information for various industries such as real estate, transportation planning, and tourism.
| Type: | Proceedings paper |
|---|---|
| Title: | A New Approach to Assessing Perceived Walkability |
| Event: | SIGSPATIAL '23: The 31st ACM International Conference on Advances in Geographic Information Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3615888.3627811 |
| Publisher version: | https://doi.org/10.1145/3615888.3627811 |
| Language: | English |
| Additional information: | © 2023 Copyright is held by the owner/author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Perceived Walkability, Zero-shot Learning, Street View Imagery, Vision-Language Model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10182328 |
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