https://orcid.org/0000-0003-4225-9435
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Abstract
Existing intervisibility analysis methods suffer from computational inefficiency due to redundant sampling points. To address this issue, we propose a new approximate method called line-of-sight (LoS) zoning, which leverages continuous terrain relief to identify potentially obscuring zones (POZ) of LoS. By limiting the sampling range to a much smaller POZ, the number of sampling points is significantly reduced. The optimal sampling interval of 6 is determined by striking a balance between computational efficiency and accuracy. Through experiments in both mountainous and plain areas, regardless of the height range and resolution conditions, we demonstrate the high efficiency of the LoS zoning method, especially in scenarios with a high proportion of visible LoS. To account for potential visibility errors caused by sharp peaks in the terrain, we conducted experiments under fixed time intervals to assess the calculation quality of different methods. The results show that in mountainous and plain areas, the improvement in detection rate compared to the hopping strategy method is around 4–6 times in most scenarios. This significant performance enhancement highlights the superiority of the LoS zoning method, and shows great promise in terrain avoidance, path planning in the military, and detection of dangerous targets.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data and codes availability statement
The data and code, as well as the user’s guide, that support the findings of this study are available with the identifier at the public links: https://doi.org/10.6084/m9.figshare.22708156
The Digital Elevation Modeling of HuNan Province with 30 × 30 meters of resolution, is downloaded from the official geospatial data repository www.gscloud.cn. And the Jiangsu DEM is downloaded from https://search.asf.alaska.edu.
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Funding
Notes on contributors
Zengjie Wang
Zengjie Wang is a Ph.D. candidate at Nanjing Normal University. His research interests include spatial analysis, GIS algorithms, and geo-modeling. He contributed to the idea, methodology, implementation, and writing.
Xiaoyu Niu
Xiaoyu Niu is a master candidate at Nanjing Normal University. Her research interests focus on complex network analysis. She contributed to the writing.
Zhenxia Liu
Zhenxia Liu is a Ph.D. candidate at Nanjing Normal University. His research interests include spatial analysis and statistical methods. He contributed to the writing.
Wen Luo
Wen Luo is a professor of Cartography and GIS at Nanjing Normal University. His research interests include geometric algebra-based GIS, GIS algorithms, and geo-modeling. He contributed to the idea, methodology, and writing.
Zhaoyuan Yu
Zhaoyuan Yu is a professor at the School of Geographical Sciences, Nanjing Normal University. His research focuses on spatiotemporal modeling of complex geographic phenomena, analysis algorithms for massive moving objects, and advanced computing technologies, such as quantum and adaptive computing. He contributed to the idea and methodology.
Jiyi Zhang
Jiyi Zhang is an associate professor at Nantong University. His research focuses on three-dimensional geography modeling, spatial analyses, and the 3D registration and management mode of real estate. He contributed to the methodology.
Linwang Yuan
Linwang Yuan is a professor at the School of Geographical Sciences, Nanjing Normal University. He is working on a unified spatiotemporal data model, multiscale geospatial analyses, and geographic knowledge discovery. He contributed to the idea and methodology.