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An interpretable machine learning framework for measuring urban perceptions from panoramic street view images

Liu, Yunzhe; Chen, Meixu; Wang, Meihui; Huang, Jing; Thomas, Fisher; Rahimi, Kazem; Mamouei, Mohammad; (2023) An interpretable machine learning framework for measuring urban perceptions from panoramic street view images. iScience , 26 (3) , Article 106132. 10.1016/j.isci.2023.106132. Green open access

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Abstract

The proliferation of street view images (SVIs) and the constant advancements in deep learning techniques have enabled urban analysts to extract and evaluate urban perceptions from large-scale urban streetscapes. However, many existing analytical frameworks have been found to lack interpretability due to their end-to-end structure and “black-box” nature, thereby limiting their value as a planning support tool. In this context, we propose a five-step machine learning framework for extracting neighborhood-level urban perceptions from panoramic SVIs, specifically emphasizing feature and result interpretability. By utilizing the MIT Place Pulse data, the developed framework can systematically extract six dimensions of urban perceptions from the given panoramas, including perceptions of wealth, boredom, depression, beauty, safety, and liveliness. The practical utility of this framework is demonstrated through its deployment in Inner London, where it was used to visualize urban perceptions at the Output Area (OA) level and to verify against real-world crime rate.

Type: Article
Title: An interpretable machine learning framework for measuring urban perceptions from panoramic street view images
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.isci.2023.106132
Publisher version: https://doi.org/10.1016/j.isci.2023.106132
Language: English
Additional information: © The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (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: Environmental sciences, Artificial Intelligence
URI: https://discovery.ucl.ac.uk/id/eprint/10167607
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