A line-of-sight zoning method for intervisibility computation by considering terrain relief

IF 4.3 1区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Geographical Information Science Pub Date : 2023-09-11 DOI:10.1080/13658816.2023.2254825
Zengjie Wang, Xiaoyu Niu, Zhenxia Liu, Wen Luo, Zhaoyuan Yu, Jiyi Zhang, Linwang Yuan
{"title":"A line-of-sight zoning method for intervisibility computation by considering terrain relief","authors":"Zengjie Wang, Xiaoyu Niu, Zhenxia Liu, Wen Luo, Zhaoyuan Yu, Jiyi Zhang, Linwang Yuan","doi":"10.1080/13658816.2023.2254825","DOIUrl":null,"url":null,"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.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":4.3000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Geographical Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/13658816.2023.2254825","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种考虑地形起伏的视距划分方法
现有的互可视性分析方法由于采样点冗余,计算效率低下。为了解决这个问题,我们提出了一种新的近似方法,称为视线(LoS)分区,该方法利用连续地形起伏来识别LoS的潜在模糊区(POZ)。通过将采样范围限制到更小的POZ,采样点的数量显着减少。最优采样间隔为6,需要在计算效率和精度之间取得平衡。通过在山区和平原地区的实验,无论高度范围和分辨率条件如何,我们都证明了LoS分区方法的高效率,特别是在可见光LoS比例较高的场景下。为了考虑地形尖峰可能造成的能见度误差,我们在固定的时间间隔内进行了实验,以评估不同方法的计算质量。结果表明,在山地和平原地区,大多数情况下,跳跃策略方法的检出率提高约为4-6倍。这种显著的性能增强突出了LoS分区方法的优越性,并在地形规避、军事路径规划和危险目标探测方面显示出巨大的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.00
自引率
7.00%
发文量
81
审稿时长
9 months
期刊介绍: International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.
期刊最新文献
GPU-accelerated parallel all-pair shortest path routing within stochastic road networks Collective flow-evolutionary patterns reveal the mesoscopic structure between snapshots of spatial network Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi’s domain adaptability Translating street view imagery to correct perspectives to enhance bikeability and walkability studies A multi-view ensemble machine learning approach for 3D modeling using geological and geophysical data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1