Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation

Bin Wang; Ping Sun; Zhongwang Zhang; Lin Ma

doi:10.1007/978-981-16-9423-3_57

Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation

Bin Wang
, Ping Sun
, Zhongwang Zhang
, Lin Ma^*

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology
China National Aeronautical Radio Electronics Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In the aspect of urban forest density estimation, there is a lack of automatic or efficient estimation methods. For the existing research on urban streetscape trees, mainly uses lidar to process point cloud data or combines deep learning to achieve tree segmentation and detection. However, these methods lead to too much computation, low efficiency, and fail to provide estimation results of urban tree density. By processing the image data, this paper proposes a tree density estimation algorithm based on image semantic segmentation, which deals with the only image data in the whole process, and realizes the estimation of tree density in the city streetscape. This algorithm is more efficient and accurate than the complex point cloud operation or the method combining point cloud with a deep learning algorithm.

Original language	English
Title of host publication	Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China
Editors	Qilian Liang, Wei Wang, Jiasong Mu, Xin Liu, Zhenyu Na
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	457-464
Number of pages	8
ISBN (Print)	9789811694226
DOIs	https://doi.org/10.1007/978-981-16-9423-3_57
State	Published - 2022
Externally published	Yes
Event	3rd International Conference on Artificial Intelligence, 2022 - Kunming, China Duration: 21 Jun 2022 → 23 Jun 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	854 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	3rd International Conference on Artificial Intelligence, 2022
Country/Territory	China
City	Kunming
Period	21/06/22 → 23/06/22

Keywords

Semantic segmentation
Tree density estimation
Urban streetscape

Access to Document

10.1007/978-981-16-9423-3_57

Cite this

Wang, B., Sun, P., Zhang, Z., & Ma, L. (2022). Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation. In Q. Liang, W. Wang, J. Mu, X. Liu, & Z. Na (Eds.), Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China (pp. 457-464). (Lecture Notes in Electrical Engineering; Vol. 854 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-9423-3_57

Wang, Bin ; Sun, Ping ; Zhang, Zhongwang et al. / Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation. Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China. editor / Qilian Liang ; Wei Wang ; Jiasong Mu ; Xin Liu ; Zhenyu Na. Springer Science and Business Media Deutschland GmbH, 2022. pp. 457-464 (Lecture Notes in Electrical Engineering).

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title = "Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation",

abstract = "In the aspect of urban forest density estimation, there is a lack of automatic or efficient estimation methods. For the existing research on urban streetscape trees, mainly uses lidar to process point cloud data or combines deep learning to achieve tree segmentation and detection. However, these methods lead to too much computation, low efficiency, and fail to provide estimation results of urban tree density. By processing the image data, this paper proposes a tree density estimation algorithm based on image semantic segmentation, which deals with the only image data in the whole process, and realizes the estimation of tree density in the city streetscape. This algorithm is more efficient and accurate than the complex point cloud operation or the method combining point cloud with a deep learning algorithm.",

keywords = "Semantic segmentation, Tree density estimation, Urban streetscape",

author = "Bin Wang and Ping Sun and Zhongwang Zhang and Lin Ma",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 3rd International Conference on Artificial Intelligence, 2022 ; Conference date: 21-06-2022 Through 23-06-2022",

year = "2022",

doi = "10.1007/978-981-16-9423-3\_57",

language = "英语",

isbn = "9789811694226",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "457--464",

editor = "Qilian Liang and Wei Wang and Jiasong Mu and Xin Liu and Zhenyu Na",

booktitle = "Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China",

address = "德国",

}

Wang, B, Sun, P, Zhang, Z & Ma, L 2022, Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation. in Q Liang, W Wang, J Mu, X Liu & Z Na (eds), Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol. 854 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 457-464, 3rd International Conference on Artificial Intelligence, 2022, Kunming, China, 21/06/22. https://doi.org/10.1007/978-981-16-9423-3_57

Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation. / Wang, Bin; Sun, Ping; Zhang, Zhongwang et al.
Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China. ed. / Qilian Liang; Wei Wang; Jiasong Mu; Xin Liu; Zhenyu Na. Springer Science and Business Media Deutschland GmbH, 2022. p. 457-464 (Lecture Notes in Electrical Engineering; Vol. 854 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation

AU - Wang, Bin

AU - Sun, Ping

AU - Zhang, Zhongwang

AU - Ma, Lin

PY - 2022

Y1 - 2022

N2 - In the aspect of urban forest density estimation, there is a lack of automatic or efficient estimation methods. For the existing research on urban streetscape trees, mainly uses lidar to process point cloud data or combines deep learning to achieve tree segmentation and detection. However, these methods lead to too much computation, low efficiency, and fail to provide estimation results of urban tree density. By processing the image data, this paper proposes a tree density estimation algorithm based on image semantic segmentation, which deals with the only image data in the whole process, and realizes the estimation of tree density in the city streetscape. This algorithm is more efficient and accurate than the complex point cloud operation or the method combining point cloud with a deep learning algorithm.

AB - In the aspect of urban forest density estimation, there is a lack of automatic or efficient estimation methods. For the existing research on urban streetscape trees, mainly uses lidar to process point cloud data or combines deep learning to achieve tree segmentation and detection. However, these methods lead to too much computation, low efficiency, and fail to provide estimation results of urban tree density. By processing the image data, this paper proposes a tree density estimation algorithm based on image semantic segmentation, which deals with the only image data in the whole process, and realizes the estimation of tree density in the city streetscape. This algorithm is more efficient and accurate than the complex point cloud operation or the method combining point cloud with a deep learning algorithm.

KW - Semantic segmentation

KW - Tree density estimation

KW - Urban streetscape

UR - https://www.scopus.com/pages/publications/85127846136

U2 - 10.1007/978-981-16-9423-3_57

DO - 10.1007/978-981-16-9423-3_57

M3 - 会议稿件

AN - SCOPUS:85127846136

SN - 9789811694226

T3 - Lecture Notes in Electrical Engineering

SP - 457

EP - 464

BT - Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China

A2 - Liang, Qilian

A2 - Wang, Wei

A2 - Mu, Jiasong

A2 - Liu, Xin

A2 - Na, Zhenyu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Artificial Intelligence, 2022

Y2 - 21 June 2022 through 23 June 2022

ER -

Wang B, Sun P, Zhang Z, Ma L. Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation. In Liang Q, Wang W, Mu J, Liu X, Na Z, editors, Artificial Intelligence in China - Proceedings of the 3rd International Conference on Artificial Intelligence in China. Springer Science and Business Media Deutschland GmbH. 2022. p. 457-464. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-9423-3_57

Urban Streetscape Tree Density Estimation Algorithm Based on Image Semantic Segmentation

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