Geovisualization: an optimization algorithm of viewpoint generation for 3D cadastral property units

Lvhua Wang, Xinxin Zhou (), Jian Shen and Shuting Zhou
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Lvhua Wang: Nanjing University of Information Science and Technology (NUIST)
Xinxin Zhou: Nanjing University of Posts and Telecommunications
Jian Shen: Nanjing Guotu Information Industry Co., Ltd
Shuting Zhou: Nanjing University of Information Science and Technology (NUIST)

Journal of Geographical Systems, 2024, vol. 26, issue 1, No 5, 116 pages

Abstract: Abstract Large amounts of geographical and property rights information are contained in 3D cadastral property units, although the amount of cadastral information expressed from various perspectives varies. Therefore, it is crucial to choose a perspective that is compatible with human visual perception and conveys the most data as the perspective changes. This paper proposes an optimization method for generating the optimal view of 3D cadastral property units to address the above issues. The construction of candidate perspectives is the first step, followed by the calculation of feature values, including visibility of 3D boundary points under each candidate perspective, visible area ratio, and visual comfort. Then, an improved particle swarm optimization technique is used to determine the optimal view of 3D cadastral property units and the ideal viewpoint for the expected input model. The research results show that the selected optimal viewpoint fully considers human visual comfort, can accommodate a large amount of cadastral information, and can be used as a mapping drawing in social applications of 3D cadastral systems. This viewpoint can effectively supplement front views, side views, and axis side views, enabling more visual information to be transmitted in 3D religion maps and 3D property right maps and improving readability.

Keywords: 3D cadastre; Property units; Particle swarm optimization; Weight function; Optimal perspective (search for similar items in EconPapers)
JEL-codes: C6 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10109-023-00429-6

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