5/29/2023 0 Comments Serious sam 2 fov![]() It is also the first cross-study for global cities. Our work enriches subjectively measured urban perception studies. These perception qualities have been identified as important in affecting pedestrian behaviors, residence move choices, and home buyer willingness to pay. To bridge the gap between AI based urban analytics and classical urban design theory, we took Shanghai as an example and applied CV and Machine Learning (ML) to subjectively measure four perceptual qualities, namely the enclosure, human scale, complexity, and imageability. Therefore, the effectiveness of subjective measures in capturing more subtle human perceptions on various urban scenes using SVI data have not been adequately addressed. In other words, their prediction of perception is a black box providing limited actionable urban design policy implications. provided an artificial intelligence (AI) based method, it was not rooted in urban design theory: the perceived safety was made based on generic image features such as color histograms or stacked HSV color channels that had been extracted from SVIs. It took raters an hour to rate a single video clip. Although the study from Ewing and Handy was well designed and based on urban design theory, their approach had low-throughput. Several studies have correlated subjective scores from raters with the objective visual elements that appeared in video clips or SVIs to successfully operationalize seemly subjective but indeed objective measures of urban perception. ![]() Despite these drawbacks in costly labor and a lack of comparability for cross-studies, subjective measures can also be effectively integrated with objectively measured indicators. There is no standard procedure to handle the involved error margin to ensure the comparability and reliability from different raters. It is more user centered although the definitions of perceptual qualities are inconsistent across studies. ![]() Ĭonversely, the “subjective measure”, which refers to evaluative scores collected from survey questions, can capture more subtle relationships. Human perceptions have subtle relationships that cannot be fully represented by individual view indices nor a simple combination of them. Only the view index of individual features such as trees and buildings are analyzed, while the overall perceptions of viewers are ignored. However, these emerging studies are still limited to objective measures. Recently, with prevalence of street view imagery (SVI) data in environmental auditing, computer vision (CV) has been widely applied to extract streetscape features, making the understanding of large-scale urban scenes possible. Conventional investigations on the quality of street design have largely relied on objective metrics, ranging from the height-to-width ratio to pedestrians counts, which require extensive spatial data and human labor for observations. Urban design qualities such as enclosure, human scale, transparency, complexity, and imageability directly affect a person’s appreciation of a street space. Streets are important public spaces for residents to thrive. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners. 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. 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. 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. We then trained ML models and achieved high accuracy in predicting scores. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. Conversely, subjective measures using survey and interview data explain human behaviors more. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. 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.
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