A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception

Abstract

The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space.

The Whole Is Other Than the Sum of Its Parts: Sensibility Analysis of 360° Urban Image Splitting

“…for one geographical location) are significantly spread out. Previous studies have often used the mean of these scores to reflect how a quality is perceived at a location (Blečić et al, 2018) or in a street (Ji et al, 2021, Zhang et al, 2018, but our results show that this statistic is not sufficient per se as a single indicator. Extremums play an important part as they report on very positive or very negative areas of the surrounding space.…”

“…As a consequence, the scores need to be aggregated to be visualized at the city scale, using maps for instance. Two aggregation levels are commonly used: at the panorama-level (Blečić et al, 2018) (average score over the four sub-images from a panorama) or at the street-level (Ji et al, 2021, Zhang et al, 2018 (average score of all the perspective images in the street).…”

“…To summarize the scores predicted on the images resulting from the panoramic splitting, previous works usually compute the average of these scores (Blečić et al, 2018, Ji et al, 2021, Zhang et al, 2018. Often, this choice is made with little justification, which led us to wonder whether it is a good indicator of the perceptual quality at the location of the panorama.…”

Street Design for Hedonistic Sustainability through AI and Human Co-Operative Evaluation

“…Furthermore, a series of statistical analyses were conducted to identify the visual elements that could cause a place to be perceived differently. Haohao et al [26] proposed a novel classification-then-regression strategy based on CNN and random forest to evaluate human perceptions of urban space. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators of the built and socio-economic environment.…”

“…It is applied to street evaluation by quantifying perceptual elements from physical elements based on the presence or absence of objects and facilities. However, studies [24][25][26][27] that quantify the perception of the urban environment focus on the continuous relationship of all information in the image, such as objects, facilities, and backgrounds, rather than the existence of individual objects. These studies are highly effective in street space evaluation by extracting specific patterns that give rise to emotions and impressions of the street space using CNN.…”