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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
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Sensors and Materials, Volume 32, Number 12(4) (2020)
Copyright(C) MYU K.K.
pp. 4559-4572
S&M2428 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3133
Published: December 29, 2020

Constrained Building Boundary Simplification Based on Partial Total Least Squares Method [PDF]

Yanmin Jin, Lingling Wei, Bin Wang, Xiongfeng Yan, Shijie Liu, and Xiaohua Tong

(Received September 30, 2020; Accepted December 15, 2020)

Keywords: partial total least squares, building simplification, polyline simplification, constraints

Maps obtained from various sensors or surveying instruments provide important geographic information for users. Buildings are critical components of various city-area maps. In this paper, we present a constrained building boundary simplification method based on the partial total least squares (PTLS) method. Simplification of building boundaries may cause some data quality problems, such as geometric displacements, right angles changed into non-right angles, and area inconsistency at different scales. Therefore, in this paper, a linear fitting model based on the PTLS method is constructed for building boundaries with the aim of reducing the total positional difference. Furthermore, end-point, right-angle, and area constraints are constructed at the same time to maintain the right angles and areas of the building. The simplification results are obtained by solving the proposed constrained model by using the PTLS method with constraints. The proposed method was applied in a building boundary simplification experiment. The results showed that the proposed method maintains the right angles and areas of buildings and reduces the geometric displacements of buildings after the simplification.

Corresponding author: Shijie Liu


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Cite this article
Yanmin Jin, Lingling Wei, Bin Wang, Xiongfeng Yan, Shijie Liu, and Xiaohua Tong, Constrained Building Boundary Simplification Based on Partial Total Least Squares Method, Sens. Mater., Vol. 32, No. 12, 2020, p. 4559-4572.



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