Preprints
https://doi.org/10.5194/essd-2022-19
https://doi.org/10.5194/essd-2022-19
 
11 Apr 2022
11 Apr 2022
Status: this preprint is currently under review for the journal ESSD.

Vectorized dataset of roadside noise barriers in China using street view imagery

Zhen Qian1,2,3, Min Chen1,2,3,4, Yue Yang1,2,3, Teng Zhong1,2,3, Fan Zhang5, Rui Zhu6, Kai Zhang1,2,3, Zhixin Zhang7,1, Zhuo Sun1,2,3, Peilong Ma1,2,3, Guonian Lü1,2,3, Yu Ye8, and Jinyue Yan9,10 Zhen Qian et al.
  • 1Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China
  • 2State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, 210023, China
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
  • 4Jiangsu Provincial Key Laboratory for NSLSCS, School of Mathematical Science, Nanjing Normal University, Nanjing, 210023, China
  • 5Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • 6Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
  • 7College of Geography & Marine, Nanjing University, Nanjing, PO Box 2100913, P.R. China
  • 8Tongji University, Department of Architecture, College of Architecture and Urban Planning, China
  • 9Future Energy Center, Malardalen University, 72123 Vasteras, Sweden
  • 10Department of Chemical Engineering, KTH Royal Institute of Technology, Stockholm, 10044, Sweden

Abstract. Roadside noise barriers (RNBs) are important urban infrastructures to develop a liveable city. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study proposes a geospatial artificial intelligence framework to create a vectorized RNB dataset in China using street view imagery. To begin, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the geo-reference to download 5.6 million Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. Subsequently, the RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2,227 km in 215 cities. At last, the quality of the RNB dataset is evaluated from two perspectives: first, the detection accuracy; second, the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10,000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F1-score of 81.44 %. Moreover, a total length of 254 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root-mean-squared error for mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08 % ± 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies. The generated vectorized RNB dataset and the labelled BSV image benchmark dataset are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).

Zhen Qian et al.

Status: open (until 29 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-19', Jimmy Zheng, 18 Apr 2022 reply
  • RC1: 'Comment on essd-2022-19', Anonymous Referee #1, 09 May 2022 reply

Zhen Qian et al.

Data sets

Vectorized dataset of roadside noise barriers in China Min Chen https://doi.org/10.11888/Others.tpdc.271914

Model code and software

NoiseBarrierIdentification Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, Jinyue Yan http://dx.doi.org/10.11888/Others.tpdc.271914

Roadside Noise Barrier Identification Zhen Qian, Min Chen, Yue Yang, Kai Zhang, Teng Zhong, Fan Zhang, Rui Zhu, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, and Yu Ye https://github.com/ChanceQZ/NoiseBarrierIdentification

Zhen Qian et al.

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Short summary
Roadside noise barriers (RNBs) are important urban infrastructures to develop a liveable city. This study provides the first reliable and nationwide vectorized RNB dataset by street view imagery in China. The generated RNB dataset is evaluated in two aspects, i.e., the detection accuracy, as well as the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.