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Data Details

Dataset of Landscape Semantic Feature Points and Visible Locations of the Great Wall of Ming Dynasty in Beijing-Tianjin-Hebei Region


LI Zhaohang1LI Renjie*1,2GUO Fenghua2,3XING Qian1
1 College of Geographical Sciences，Hebei Normal University，Shijiazhuang 050024，China2 Key Research Base of Humanities and Social Sciences in Higher Educational Institutions in Hebei Province，GeoComputation and Planning Center of Hebei Normal University，Shijiazhuang 050024，China3 Institute of Geographical Sciences，Hebei Academy of Sciences，Hebei Technology Innovation Center for Geographic Information Application，Shijiazhuang 050011，China

DOI:10.3974/geodb.2025.10.02.V1

CSTR:20146.11.2025.10.02.V1

Published:Oct. 2025

Visitors：5553       Data Files Downloaded：81      
Data Downloaded：56723.85 MB      Citations：

Key Words：

visible location,landscape,semantic feature points,Great Wall of Ming Dynasty,Beijing-Tianjin-Hebei

Abstract：

Against the background of the construction of national cultural parks, landscape planning and design for the Great Wall needs to address the question of “where landscapes can be seen” from the perspective of resource value. The key lies in the expression of multi-dimensional semantics of landscapes and the calculation of visual locations. This study selects the Ming Great Wall in Beijing-Tianjin-Hebei region, and divides it into 22 sections based on historical continuity and administrative divisions. Using basic data such as DEM and cultural relics’ surveys, the spatial locations and multi-dimensional semantics of the Great Wall landscape are summarized and expressed through 3 types of feature points: functional facility points, morphological feature points, and equidistant encrypted points. A total of 53,454 feature points were extracted and semantically coded. Combined with DEM data, visual calculations and organization were performed for these feature points within a 10-km range, and multi-dimensional data of visual locations were established for each section through 3 dimensions: X, Y, and semantic feature points. Field verification in the Gubeikou section shows that the average coincidence rates of the number and content of feature points in visual locations are 76.37% and 70.69%, respectively, indicating that the dataset has high credibility. The dataset consists of 2 parts: (1) vector data (in .shp format) of wall elements and semantic feature points of the Ming Great Wall in Beijing-Tianjin-Hebei Region; (2) visual location data (in .nc format) of the Ming Great Wall in Beijing-Tianjin-Hebei Region, with a resolution of 12.5 m. The dataset is composed of 38 data files with data size of 7.83 GB (compressed into 3 files, 2.00 GB).

Foundation Item：

Natural Science Foundation of Hebei Province (D2023205011); National Natural Science Foundation of China (41471127)

Data Citation：

LI Zhaohang, LI Renjie*, GUO Fenghua, XING Qian. Dataset of Landscape Semantic Feature Points and Visible Locations of the Great Wall of Ming Dynasty in Beijing-Tianjin-Hebei Region[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.10.02.V1.
CSTR:20146.11.2025.10.02.V1.

Related Publication：

Li, Z. H., Xing, Q., Guo, F. H., et al. Extraction of multidimensional semantic feature points and landscape visible location computing of lineal cultural heritage [J]. Journal of Geo-information Science, 2025, 27(7): 1721-1737. DOI: 10.12082/dqxxkx.2025.250104.
     

References：


     [1] Guo, F. H., Cheng L. P., Fu, X. Q., et al. Calculation model of tourist’s landscape perception degree within destination based on grid data structure [J]. Areal Research and Development, 2018, 37(1): 125-130.
     [2] Guo, F. H., Sun, B. L., Li, J. H., et al. The Great Wall visual landscape resources and its perception location calculation method [J]. Geography and Geo-information Science, 2022, 38(6): 9-16.
     [3] Wang, P., Pan, G. H., Gao, F. Q. Gestalt Psychology [M]. Ji’nan: Shandong Education Press, 2009.
     [4] Liu, F. F., Kang, J., Wu, Y., et al. What do we visually focus on in a world heritage site? a case study in the historic centre of Prague [J]. Humanities and Social Sciences Communications, 2022, 9(1): 400.
     [5] He, D., Lu, L. N., Wang, J., et al. Landscape character identification in large-scale linear heritage areas: a case study of Beijing Great Wall cultural belt [J]. Landscape Architecture, 2022, 29(9): 99-106.
     [6] Wang, Y. M. Feature point selecting of vector curve [J]. Engineering of Surveying and Mapping, 2002(2): 8-10.
     [7] Ervin, S., Steinitz, C. Landscape visibility computation: necessary, but not sufficient [J]. Environment & Planning B Planning & Design, 2003, 30(5): 757-766.
     [8] Li, R. J., Lu, Z., Li, J. F. The calculation method of landscape perception sensitivity on sightseeing route in ecotourism destinations: a case study of Qixiagu scenic region in Wu’an National Geopark [J]. Acta Geographica Sinica, 2011, 66(2): 244-256.
     [9] Sun, B. L., Guo, F. H., Li, R. J., et al. Linear cultural heritage landscape visual perception location model and demonstration [J]. Progress in Geography, 2024, 43(1): 80-92.
     [10] Li, Z. H., Li, R. J., Sun, B. L., et al. Visual perception location dataset of Gubeikou Great Wall [J]. Journal of Global Change Data & Discovery, 2024, 8(1): 32-41.
     [11] Li, Z. H., Xing, Q., Guo, F. H., et al. Extraction of multidimensional semantic feature points and landscape visible location computing of lineal cultural heritage [J]. Journal of Geo-information Science, 2025, 27(7): 1721-1737.

Data Product:

ID Data Name Data Size Operation
1 GWMBTH_VL_1.rar 762970.79KB
DownLoad
2 GWMBTH_VL_2.rar 702001.85KB
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3 GWMBTH_VL_3.rar 638018.22KB
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