Congestion Analysis Based on Remote Sensing Images

Hanning Yuan; Jiakai Yang; Xiaolei Li; Shengyu Ma

doi:10.1007/978-981-13-0893-2_37

Congestion Analysis Based on Remote Sensing Images

Hanning Yuan^*
, Jiakai Yang
, Xiaolei Li
, Shengyu Ma

^*Corresponding author for this work

Beijing Institute of Technology

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

Abstract

Most Congestion analysis are based on the urban traffic video surveillance, which depend on the quality of existing surveillance equipments. In this paper, we propose a novel method to perform congestion analysis by utilizing remote sensing images for undeveloped areas or disaster-affected areas where lack of traffic video surveillance. Firstly, the vehicles and extract road area is detected from remote sensing images using objects detection technique. Then the number of Vehicles in the road are counted and mapped into data instances. Finally, density-based clustering algorithm is adopted to find the locations which are probably the Congestion points. The experimental results on real world datasets demonstrate that the proposed method can perform congestion analysis effectively.

Original language	English
Title of host publication	Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers
Editors	Fuling Bian, Hanning Yuan, Jing Geng, Chuanlu Liu, Tisinee Surapunt
Publisher	Springer Verlag
Pages	345-352
Number of pages	8
ISBN (Print)	9789811308925
DOIs	https://doi.org/10.1007/978-981-13-0893-2_37
Publication status	Published - 2018
Externally published	Yes
Event	5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017 - Chiang Mai, Thailand Duration: 8 Dec 2017 → 10 Dec 2017

Publication series

Name	Communications in Computer and Information Science
Volume	848
ISSN (Print)	1865-0929

Conference

Conference	5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017
Country/Territory	Thailand
City	Chiang Mai
Period	8/12/17 → 10/12/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Congestion analysis
Remote sensing image
Road extraction
Vehicle detection

Access to Document

10.1007/978-981-13-0893-2_37

Cite this

Yuan, H., Yang, J., Li, X., & Ma, S. (2018). Congestion Analysis Based on Remote Sensing Images. In F. Bian, H. Yuan, J. Geng, C. Liu, & T. Surapunt (Eds.), Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers (pp. 345-352). (Communications in Computer and Information Science; Vol. 848). Springer Verlag. https://doi.org/10.1007/978-981-13-0893-2_37

Yuan, Hanning ; Yang, Jiakai ; Li, Xiaolei et al. / Congestion Analysis Based on Remote Sensing Images. Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers. editor / Fuling Bian ; Hanning Yuan ; Jing Geng ; Chuanlu Liu ; Tisinee Surapunt. Springer Verlag, 2018. pp. 345-352 (Communications in Computer and Information Science).

@inproceedings{dd456fed339e444ab32df9fbd6860865,

title = "Congestion Analysis Based on Remote Sensing Images",

abstract = "Most Congestion analysis are based on the urban traffic video surveillance, which depend on the quality of existing surveillance equipments. In this paper, we propose a novel method to perform congestion analysis by utilizing remote sensing images for undeveloped areas or disaster-affected areas where lack of traffic video surveillance. Firstly, the vehicles and extract road area is detected from remote sensing images using objects detection technique. Then the number of Vehicles in the road are counted and mapped into data instances. Finally, density-based clustering algorithm is adopted to find the locations which are probably the Congestion points. The experimental results on real world datasets demonstrate that the proposed method can perform congestion analysis effectively.",

keywords = "Congestion analysis, Remote sensing image, Road extraction, Vehicle detection",

author = "Hanning Yuan and Jiakai Yang and Xiaolei Li and Shengyu Ma",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; 5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017 ; Conference date: 08-12-2017 Through 10-12-2017",

year = "2018",

doi = "10.1007/978-981-13-0893-2\_37",

language = "English",

isbn = "9789811308925",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "345--352",

editor = "Fuling Bian and Hanning Yuan and Jing Geng and Chuanlu Liu and Tisinee Surapunt",

booktitle = "Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers",

address = "Germany",

}

Yuan, H, Yang, J, Li, X & Ma, S 2018, Congestion Analysis Based on Remote Sensing Images. in F Bian, H Yuan, J Geng, C Liu & T Surapunt (eds), Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers. Communications in Computer and Information Science, vol. 848, Springer Verlag, pp. 345-352, 5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017, Chiang Mai, Thailand, 8/12/17. https://doi.org/10.1007/978-981-13-0893-2_37

Congestion Analysis Based on Remote Sensing Images. / Yuan, Hanning; Yang, Jiakai; Li, Xiaolei et al.
Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers. ed. / Fuling Bian; Hanning Yuan; Jing Geng; Chuanlu Liu; Tisinee Surapunt. Springer Verlag, 2018. p. 345-352 (Communications in Computer and Information Science; Vol. 848).

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

TY - GEN

T1 - Congestion Analysis Based on Remote Sensing Images

AU - Yuan, Hanning

AU - Yang, Jiakai

AU - Li, Xiaolei

AU - Ma, Shengyu

PY - 2018

Y1 - 2018

N2 - Most Congestion analysis are based on the urban traffic video surveillance, which depend on the quality of existing surveillance equipments. In this paper, we propose a novel method to perform congestion analysis by utilizing remote sensing images for undeveloped areas or disaster-affected areas where lack of traffic video surveillance. Firstly, the vehicles and extract road area is detected from remote sensing images using objects detection technique. Then the number of Vehicles in the road are counted and mapped into data instances. Finally, density-based clustering algorithm is adopted to find the locations which are probably the Congestion points. The experimental results on real world datasets demonstrate that the proposed method can perform congestion analysis effectively.

AB - Most Congestion analysis are based on the urban traffic video surveillance, which depend on the quality of existing surveillance equipments. In this paper, we propose a novel method to perform congestion analysis by utilizing remote sensing images for undeveloped areas or disaster-affected areas where lack of traffic video surveillance. Firstly, the vehicles and extract road area is detected from remote sensing images using objects detection technique. Then the number of Vehicles in the road are counted and mapped into data instances. Finally, density-based clustering algorithm is adopted to find the locations which are probably the Congestion points. The experimental results on real world datasets demonstrate that the proposed method can perform congestion analysis effectively.

KW - Congestion analysis

KW - Remote sensing image

KW - Road extraction

KW - Vehicle detection

UR - https://www.scopus.com/pages/publications/85049022715

U2 - 10.1007/978-981-13-0893-2_37

DO - 10.1007/978-981-13-0893-2_37

M3 - Conference contribution

AN - SCOPUS:85049022715

SN - 9789811308925

T3 - Communications in Computer and Information Science

SP - 345

EP - 352

BT - Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers

A2 - Bian, Fuling

A2 - Yuan, Hanning

A2 - Geng, Jing

A2 - Liu, Chuanlu

A2 - Surapunt, Tisinee

PB - Springer Verlag

T2 - 5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

Y2 - 8 December 2017 through 10 December 2017

ER -

Congestion Analysis Based on Remote Sensing Images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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