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Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai

1
Department of City and Regional Planning, Cornell University, Ithaca, NY 14850, USA
2
Center for Spatial Information Science, The University of Tokyo, Tokyo 113-8654, Japan
3
School of Architecture, University of Virginia, Charlottesville, VA 22904, USA
4
Graduate School of Design, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(8), 493; https://doi.org/10.3390/ijgi10080493
Received: 23 May 2021 / Revised: 17 July 2021 / Accepted: 19 July 2021 / Published: 21 July 2021
Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners. View Full-Text
Keywords: subjective measure; human perception; street view image; computer vision; global comparison subjective measure; human perception; street view image; computer vision; global comparison
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MDPI and ACS Style

Qiu, W.; Li, W.; Liu, X.; Huang, X. Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai. ISPRS Int. J. Geo-Inf. 2021, 10, 493. https://doi.org/10.3390/ijgi10080493

AMA Style

Qiu W, Li W, Liu X, Huang X. Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai. ISPRS International Journal of Geo-Information. 2021; 10(8):493. https://doi.org/10.3390/ijgi10080493

Chicago/Turabian Style

Qiu, Waishan, Wenjing Li, Xun Liu, and Xiaokai Huang. 2021. "Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai" ISPRS International Journal of Geo-Information 10, no. 8: 493. https://doi.org/10.3390/ijgi10080493

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