Modeling and mining for multiscale visibility data of landscapes
Source abstract
Abstract
Abstract Current landscape visibility data lacks systematic organization and deep mining. This study proposes a multiscale landscape–ground position visibility model, constructing a 4D tensor integrating visibility relationships across multiple viewing radii. Mining patterns like Minimum Coverage Equivalent Cell Classes (MCECC) and Minimum Coverage Visibility Convex Polygons (MCVCP) are designed to calculate landscape visual protection zones based on minimal observation positions. Using the Gubeikou Great Wall as a case study, a multiscale visibility dataset was constructed. Results show that for the Jinshanling section, MCVCP reduces the required protection area by 90% and 70% compared to the buffer zone and Reverse Zones of Theoretical Visibility (R-ZTV) methods, respectively. For Gubeikou, the reductions are 73% and 49%. Furthermore, complete visual coverage is achieved with a maximum of only 4 and 12 observation points for the two sites, respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geographic Information Systems Studies · Urban Green Space and Health · Data Visualization and Analytics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
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1. Wartmann, F. M., Frick, J., Kienast, F. & Hunziker, M. Factors influencing visual landscape quality perceived by the public. Results from a national survey. Landsc. Urban Plan 208, 104024 (2021).10.1016/j.landurbplan.2020.104024 ↗
2. Sharafatmandrad, M. & Khosravi Mashizi, A. Visual value of rangeland landscapes: a study based on structural equation modeling. Ecol. Eng. 146, 105742 (2020).10.1016/j.ecoleng.2020.105742 ↗
3. Henriques, R. J. et al. Soils and landscapes of Marajó island, Brazilian Amazonia: Holocene evolution, geoarchaeology and climatic vulnerability. Environ. Earth sci. 81, 254 (2022).10.1007/s12665-022-10310-2 ↗
4. Fábrega-Álvarez, P. & Parcero-Oubiña, C. Now you see me. An assessment of the visual recognition and control of individuals in archaeological landscapes. J. Archaeol. Sci. 104, 56–74 (2019).10.1016/j.jas.2019.02.002 ↗
5. Garcia-Moreno, A. To see or to be seen… is that the question? An evaluation of palaeolithic sites’ visual presence and their role in social organization. J. Anthropol. Archaeol. 32, 647–658 (2013).10.1016/j.jaa.2013.03.003 ↗
6. Inglis, N. C., Vukomanovic, J., Costanza, J. & Singh, K. K. From viewsheds to viewscapes: Trends in landscape visibility and visual quality research. Landsc. Urban Plan 224, 104424 (2022).10.1016/j.landurbplan.2022.104424 ↗
7. Ruzickova, K., Ruzicka, J. & Bitta, J. A new GIS-compatible methodology for visibility analysis in digital surface models of earth sites. Geosci. Front. 12, 101109 (2021).10.1016/j.gsf.2020.11.006 ↗
8. Wang, Y. & Dou, W. A fast candidate viewpoints filtering algorithm for multiple viewshed site planning. Int. J. Geogr. Inf. Sci. 34, 448–463 (2020).10.1080/13658816.2019.1664743 ↗
9. Guan, L., Wu, C., Xia, Q., Chen, G. & Li, A. Fast approximate viewshed analysis based on the regular-grid digital elevation model: X-type partition proximity-direction-elevation spatial reference line algorithm. Comput. Geosci. 167, 105213 (2022).10.1016/j.cageo.2022.105213 ↗
10. Fisher, P. F. Algorithm and implementation uncertainty in viewshed analysis. Int. J. Geogr. Inf. Syst. 7, 331–347 (1993).10.1080/02693799308901965 ↗
11. Salehi, A., Halabian, A., Ahmadi Beni, F., Salehi, M. & Dadras, B. A fast serial computation approach for computing viewshed of a region by detecting key spots on the terrain. Geocarto Int. 37, 17625–17641 (2022).10.1080/10106049.2022.2129841 ↗
12. Ucero, A., Alonso, J. C., Palacín, C., Abril-Colón, I. & Álvarez-Martínez, J. M. Display site selection in a ground dwelling bird: the importance of viewshed. Behav. Ecol. 34, 223–235 (2023).10.1093/beheco/arac112 ↗
13. Ma, X., Zhang, X. & Zhao, X. Service coverage optimization for facility location: considering line-of-sight coverage in continuous demand space. Int. J. Geogr. Inf. Sci. 37, 1496–1519 (2023).10.1080/13658816.2023.2193829 ↗
14. Yao, Y. et al. A site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data. T. GIS 27, 18 (2023).
15. Zhang, C., Wang, Y., Cheng, H. & Dou, W. Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method. ISPRS Int. J. Geo-Inf. 14, 113 (2025).10.3390/ijgi14030113 ↗
16. Sanchez-Fernandez, A. J., Romero, L. F., Bandera, G. & Tabik, S. VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 944–955 (2022).10.1109/JSTARS.2021.3134948 ↗
17. Cimburova, Z. & Blumentrath, S. Viewshed-based modelling of visual exposure to urban greenery – An efficient GIS tool for practical planning applications. Landsc. Urban Plan 222, 104395 (2022).10.1016/j.landurbplan.2022.104395 ↗
18. Lehto, C., Hedblom, M., Filyushkina, A. & Ranius, T. Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning. Landsc. Urban Plan 248, 105097 (2024).10.1016/j.landurbplan.2024.105097 ↗
19. Wheatley, D. in Archaeology and Geographic Information Systems. 171–185 (CRC Press, 2022).10.1201/9780367810467-13 ↗
20. Rothenberg, M., Leppard, T. P., Weitzel, E. & Murphy, E. A. Settlement organization and distribution in bronze age sardinia: utilizing cumulative viewshed analysis and spatial statistics in the Sulcis Plain. J. Field Archaeol. 49, 356–370 (2024).10.1080/00934690.2024.2318682 ↗
21. Baumann, P. A general conceptual framework for multi-dimensional spatio-temporal data sets. Environ. Modell. Softw. 143, 105096 (2021).10.1016/j.envsoft.2021.105096 ↗
22. Iqbal, M., Lissandrini, M. & Pedersen, T. B. A foundation for spatio-textual-temporal cube analytics. Inform. Syst. 108, 102009 (2022).10.1016/j.is.2022.102009 ↗
23. Fang, R., Xiong, K., Ding, Y. & Wang, D. Integration of the Natural World Heritage conservation and development of buffer zone agroforestry: from scientometrics insights and implications for the Karst World Heritage. Herit. Sci. 12, 338 (2024).10.1186/s40494-024-01453-5 ↗
24. Sukwai, J., Mishima, N. & Srinurak, N. Balancing cultural heritage conservation: visual integrity assessment to support change management in the buffer zone of Chiang Mai historic city using GIS and computer-generated 3D modeling. Land 11, 666 (2022).10.3390/land11050666 ↗
25. Zhang, J., Shi, Y., Zhao, L., Cai, C. & Furuya, K. Toward sustainable and differentiated protection of cultural heritage illustrated by a multisensory analysis of Suzhou and Kyoto using deep learning. npj Herit. Sci. 13, 287 (2025).10.1038/s40494-025-01855-z ↗
26. Zu, X., Gao, C. & Wang, Y. Towards religious landscape: protection of Tibetan Buddhist heritage in Aba Prefecture. Int. J. Appl. Earth Observ. Geoinf. 130, 103885 (2024).
27. Ma, G. et al. Analysis of landscape pattern evolution and driving forces based on land-use changes: a case study of Yilong lake watershed on Yunnan-Guizhou plateau. Land 11, 1276 (2022).10.3390/land11081276 ↗
28. Di Ruocco, I., D’Auria, A., D’Alterio, R. R. & De Rosa, A. A framework for a user-perception-based approach to integrate landscape protection in soft mobility planning. Land 12, 1048 (2023).10.3390/land12051048 ↗
29. Wells, M. P. & Brandon, K. E. The principles and practice of buffer zones and local participation in biodiversity conservation. Ambio 22, 157–162 (1993).
30. De Souza, J. C., Mendes, T. S. G., Bignotto, R. B., de Alcântara, E. H. & Massi, K. G. Land use dynamics in a tropical protected area buffer zone: is the management plan helping? J. Environ. Stud. Sci. 15, 156–166 (2025).10.1007/s13412-024-00905-5 ↗
31. Vanhuele, N. & Vanneste, D. How significant are buffer zones for tourism at urban UNESCO world heritage sites? Heritage 8, 105 (2025).10.3390/heritage8030105 ↗
32. Tsani, T., Weinand, J. M., Linßen, J. & Stolten, D. Quantifying social factors for onshore wind planning – A systematic review. Renew. Sustain. Energy Rev. 203, 114762 (2024).10.1016/j.rser.2024.114762 ↗
33. Ioannidis, R., Mamassis, N., Efstratiadis, A. & Koutsoyiannis, D. Reversing visibility analysis: Towards an accelerated a priori assessment of landscape impacts of renewable energy projects. Renew. Sustain. Energy Rev. 161, 112389 (2022).10.1016/j.rser.2022.112389 ↗
34. Tsani, T. et al. Quantifying the trade-offs between renewable energy visibility and system costs. Nat. Commun. 16, 3853 (2025).10.1038/s41467-025-59029-1 ↗
35. Zhao, S., Yang, D. & Gao, C. Identifying landscape character for large linear heritage: a case study of the Ming Great Wall in Ji-Town, China. Sustainability 15, 2615 (2023).10.3390/su15032615 ↗
36. Lemke, C. E., Salkin, H. M. & Spielberg, K. Set covering by single-branch enumeration with linear-programming subproblems. Oper. Res. 19, 998–1022 (1971).10.1287/opre.19.4.998 ↗
37. Wu, C., Guan, L., Xia, Q., Chen, G. & Shen, B. PDERL: an accurate and fast algorithm with a novel perspective on solving the old viewshed analysis problem. Earth Sci. Inform. 14, 619–632 (2021).10.1007/s12145-020-00545-7 ↗
38. Hashemi, A., Gholami, H., Delorme, X. & Wong, K. Y. A multidimensional fitness function based heuristic algorithm for set covering problems. Appl. Soft Comput. 174, 113038 (2025).10.1016/j.asoc.2025.113038 ↗
39. Xu, H., Chen, F. & Zhou, W. A comparative case study of MTInSAR approaches for deformation monitoring of the cultural landscape of the Shanhaiguan section of the Great Wall. Herit. Sci. 9, 1–15 (2021).10.1186/s40494-021-00543-y ↗
40. National Center for Atmospheric Research. Unidata: Network Common Data Form (NetCDF). Boulder, CO, https://doi.org/10.5065/D6H70CW6 (2017).10.5065/D6H70CW6 ↗
41. Liu, Y. & Wang, Q. Research on meteorological data storage technology based on NetCDF. in IEEE International Conference on Mechatronics and Automation (ICMA) 941–945 (IEEE, 2024).10.1109/ICMA61710.2024.10633094 ↗
42. Sun, B., Guo, F., Li, R. & Chen, X. Linear cultural heritage landscape visual perception location model and demonstration [In Chinese]. Prog. Geogr. 43, 1 (2024).
43. Wakil, M. A., Sun, Y. & Chan, E. H. W. Co-flourishing: intertwining community resilience and tourism development in destination communities. Tour. Manag. Perspect. 38, 100803 (2021).
44. Heagney, E. C., Rose, J. M., Ardeshiri, A. & Kovac, M. The economic value of tourism and recreation across a large protected area network. Land Use Policy 88, 104084 (2019).10.1016/j.landusepol.2019.104084 ↗
45. Zhu, H. et al. Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework. J. Clean. Prod. 456, 142306 (2024).10.1016/j.jclepro.2024.142306 ↗
46. Zhu, S. et al. Spatiotemporal analysis of the impact of urban landscape forms on PM2.5 in China from 2001 to 2020. Int. J. Digit. Earth 16, 3417–3434 (2023).10.1080/17538947.2023.2249862 ↗
47. Tang, G. et al. An efficient and precise multi-candidate viewpoint filtering algorithm for terrain viewshed selection. Int. J. Geogr. Inf. Sci. 39, 400–421 (2025).10.1080/13658816.2024.2408279 ↗
48. Ramalho, E. et al. Understanding wind Energy Economic externalities impacts: a systematic literature review. Renew. Sustain. Energy Rev. 209, 115120 (2025).10.1016/j.rser.2024.115120 ↗