Sensing urban soundscapes from street view imagery

Zhao, Tianhong; Liang, Xiucheng; Tu, Wei; Huang, Zhengdong; Biljecki, Filip

doi:10.1016/j.compenvurbsys.2022.101915

Cited by 47 publications

(6 citation statements)

References 74 publications

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“…The models we selected are classical and well-established in supervised learning. They have been widely used as predictions based on street image feature in urban-related studies [84,85]. The dependent variables, five for males and five for females, were perceived walkability scores obtained through the online questionnaire collection platform; the independent variables were the top 15 influencing factors of each model evaluated by GINI.…”

Section: Prediction Models Comparisonmentioning

confidence: 99%

Walkability Perceptions and Gender Differences in Urban Fringe New Towns: A Case Study of Shanghai

Wenjing

Huang

White

et al. 2023

Land

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Urban fringe areas, characterized by relatively larger community sizes and lower population densities compared to central areas, may lead to variations in walkability as well as gender differences, such as safety perception. While objective measurements have received considerable attention, further research is needed to comprehensively assess subjective perceptions of walking in the urban periphery. As a case study, we evaluated survey responses of community perceptions of “Imageability”, “Enclosure”, “Human scale”, “Complexity” and “Safety” of Shanghai’s five new towns, comparing these with responses from the central area in terms of gender difference, and analyzed influencing factors and prediction performance of machine learning (ML) models. We developed a TrueSkill-based rating system to dynamically collect audits of street view images (SVIs) from professional students and used the result to integrate with Geographic Information Systems (GIS), Computer Vision (CV), Clustering analysis, and ML algorithm for further investigation. Results show that most of the new towns’ communities are perceived as moderately walkable or higher, with the city center’s community exhibiting the best walkability perceptions in general. Male and female perceptions of the “Human scale” and the factors that affect it differ little, but there are significant disparities in the other four perceptions. The best-performing ML models were effective at variable explanations and generalizations, with Random Forest Regression (RFR) performing better on more perception predictions. Responses also suggest that certain street design factors, such as street openness, can positively influence walkability perceptions of women and could be prioritized in new town development and urban renewal for more inclusive and walkable cities.

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Section: Prediction Models Comparisonmentioning

confidence: 99%

Walkability Perceptions and Gender Differences in Urban Fringe New Towns: A Case Study of Shanghai

Wenjing

Huang

White

et al. 2023

Land

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show abstract

“…In “Smart City Intersections: Intelligence Nodes for Future Metropolises” [ 1 ], Kostec et al detail intersections as intelligence nodes using high-bandwidth, low-latency services for monitoring pedestrians and cloud-connected vehicles in real-time. Other computer vision applications to urban street view imagery include extracting visual features to create soundscape maps [ 34 ], mapping trees along urban street networks [ 35 ], estimating pedestrian density [ 36 ] and volume [ 37 ], associating sounds with their respective objects in video [ 10 ], and geolocating objects from a combination of street-level and overhead imagery [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset

Piadyk

Rulff

Brewer

et al. 2023

Sensors

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Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.

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“…Messier et al (2018) mapped spatial air quality patterns using an unusually rich data set of repeated mobile air quality measurements collected with specially equipped Google Street View cars . Furthermore, deep learning-based computer vision algorithms have achieved notable advancements, gaining broad recognition and triumph across diverse domains owing to their remarkable ability for autonomous learning and representation of image features. − As of now, image-based pollution models have been essentially developed in various regions worldwide. − The majority of research techniques employ image segmentation to quantify urban streetscapes. , …”

Section: Introductionmentioning

confidence: 99%

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery

Liu,

Zhang,

Wang

et al. 2024

Environ. Sci. Technol.

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In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM 1.0 , PM 2.5 , and PM 10 , particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.

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Sensing urban soundscapes from street view imagery

Cited by 47 publications

References 74 publications

Walkability Perceptions and Gender Differences in Urban Fringe New Towns: A Case Study of Shanghai

Walkability Perceptions and Gender Differences in Urban Fringe New Towns: A Case Study of Shanghai

StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery

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