ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume V-2-2022
https://doi.org/10.5194/isprs-annals-V-2-2022-375-2022
https://doi.org/10.5194/isprs-annals-V-2-2022-375-2022
17 May 2022
 | 17 May 2022

MAPPING STREET OBSTRUCTIONS IN AN URBAN STREET ENVIRONMENT USING MLS DATA

C. Liu and Y. Qi

Keywords: MLS data, street obstruction, non-obstruction space, obstruction analysis, urban street environment

Abstract. It is crucial to ensure no obstruction above urban streets for drivers. If street obstructions limit the sight of drivers, it may reduce the ability of drivers to perceive the surrounding road conditions. Street obstructions are usually caused by a variety of different factors. It includes street trees overgrowing into the space above the road or the wrong placement of the infrastructure. To ensure road traffic safety, transportation agencies are required to obtain the existence of street obstructions. To address the issue, this paper proposes a novel method to detect street obstructions. The method consists of two main steps: non-obstruction space construction by the Alpha-Smooth method and street obstruction analysis. The proposed approach is able to map the thematic map of street obstructions in the urban environment. The algorithm was tested on urban streets in Shanghai, China, and successfully obtained obstructions information. The results indicated that street trees are the main components of road obstructions. The proposed approach can make contribution to street maintenance and obstructions monitoring that aim to develop safer urban street environments.