A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs

CHEN Luanjie; LI Weichao; PENG Ling; CHEN Jiahui; GAO Xiang

doi:10.6046/zrzyyg.2022022

2023 Vol. 35, No. 1

Article Contents

Article navigation > Remote Sensing for Natural Resources > 2023 > 35(1): 243-250

Next Article Previous Article

CHEN Luanjie, LI Weichao, PENG Ling, CHEN Jiahui, GAO Xiang. 2023. A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs. Remote Sensing for Natural Resources, 35(1): 243-250. doi: 10.6046/zrzyyg.2022022

Citation:

CHEN Luanjie, LI Weichao, PENG Ling, CHEN Jiahui, GAO Xiang. 2023. A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs. Remote Sensing for Natural Resources, 35(1): 243-250. doi: 10.6046/zrzyyg.2022022

A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China；
2. College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China；
3. Tianxin Pavilion Big Data Institute, Changsha 410000, China

Received Date: 17 January 2022

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

Accurate and efficient quality inspection and database updates of cadastral data are essential for natural resource management. The current cadastral data management faces problems such as the low efficiency of quality inspection and updates, difficulty in meeting the demand for dynamic supervision, and small application scopes of relevant methods. To solve these problems, this study proposed a method framework based on spatio-temporal knowledge graphs. Moreover, with cadastral data and remote sensing images as data sources, this study constructed a spatio-temporal knowledge graph targeting the quality inspection and update workflow of cadastral data by designing conceptual and data layers and inference rules. Finally, experiments on the method proposed in this study were conducted using seven parcels of land in Changsha. As a result, the common errors in the process of quality inspection and updates were solved, and the method proposed in this study was proven to be more efficient than common methods.
- spatio-temporal knowledge graph /
- cadastral data /
- cadastral database

FullText(HTML)

References

[1]	Coscieme L, Niccolucci V, Giannetti B F, et al. Implications of land-grabbing on the ecological balance of Brazil[J]. Resources, 2018, 7(3): 44. Google Scholar
[2]	Ciencia?a A, Sobolewska-Mikulska K, Sobura S. Credibility of the cadastral data on land use and the methodology for their verification and update[J]. Land Use Policy, 2021, 102(3):105204. Google Scholar
[3]	Oregi X, Hermoso N, Prieto I, et al. Automatised and georeferenced energy assessment of an Antwerp district based on cadastral data[J]. Energy and Buildings, 2018, 173(16):176-194. Google Scholar
[4]	Silva M A, Stubkj?r E. A review of methodologies used in research on cadastral development[J]. Computers,Environment and Urban Systems, 2002, 26(5):403-423. Google Scholar
[5]	Williamson I. Using the case study methodology for cadastral reform[J]. Geomatica, 1998, 52(3):283-295. Google Scholar
[6]	Christensen D, Garfias F. The politics of property taxation:Fiscal infrastructure and electoral incentives in Brazil[J]. The Journal of Politics, 2021, 83(4):1399-1416. Google Scholar
[7]	韩文立, 张莉, 程鹏飞. 地理信息质检数据库建设和应用的技术探讨[J]. 测绘通报, 2015, 61(3): 94-96. Google Scholar
[8]	Han W L, Zhang L, Cheng P F. Investigations of construction and application technology for geographic information quality inspection database[J]. Bulletin of Surveying and Mapping, 2021, 61(3):94-96. Google Scholar
[9]	王金栋, 韩文立, 章立博, 等. 基于已有资料的自动质检技术研究与实现[J]. 测绘通报, 2017, 63(2):109-111. Google Scholar
[10]	Wang J D, Han W L, Zhang L B, et al. Research and implementation of automatic quality control technology based on existing material data[J]. Bulletin of Surveying and Mapping, 2017, 63(2):109-111. Google Scholar
[11]	Joy J, Kanga S, Singh S K, et al. Cadastral level soil and water conservation priority zonation using geospatial technology[J]. International Journal of Agriculture System, 2021, 9(1):10-26. Google Scholar
[12]	邱冬炜, 杨松林. 地籍信息系统数据库的构建[J]. 地球信息科学, 2004, 9(3): 43-45,50. Google Scholar
[13]	Qiu D W, Yang S L. The establishment of database for cadastral information system based on GIS[J]. Journal of Geo-Information Science, 2004, 9(3):43-45,50. Google Scholar
[14]	李志刚, 艾廷华. 时态GIS在地籍变更管理信息系统中的应用研究[J]. 测绘通报, 2003, 49(6): 58-60. Google Scholar
[15]	Li Z G, Ai T H. Application research on temporal GIS in the cadastral alteration management system[J]. Bulletin of Surveying and Mapping, 2003, 49(6):58-60. Google Scholar
[16]	张丰, 刘南, 刘仁义, 等. 面向对象的地籍时空过程表达与数据更新模型研究[J]. 测绘学报, 2010, 39(3): 303-309. Google Scholar
[17]	Zhang F, Liu N, Liu R Y, et al. Research of cadastral data modelling and database updating based on spatio-temporal process[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(3):303-309. Google Scholar
[18]	Chen X, Jia S, Xiang Y. A review:Knowledge reasoning over knowledge graph[J]. Expert Systems with Applications, 2020, 141(5):112948. Google Scholar
[19]	Chen J H, Ge X T, Li W C, et al. Construction of spatiotemporal knowledge graph for emergency decision making[C]// IEEE International Geoscience and Remote Sensing Symposium, 2021:3920-3923. Google Scholar
[20]	Angles R, Gutierrez C. Survey of graph database models[J]. ACM Computing Surveys (CSUR), 2008, 40(1):1-39. Google Scholar
[21]	McGuinness D L, Van Harmelen F. OWL web ontology language overview[J]. W3C recommendation, 2004, 10(10):2004. Google Scholar
[22]	Battle R, Kolas D. Geosparql:Enabling a geospatial semantic web[J]. Semantic Web Journal, 2011, 3(4):355-370. Google Scholar
[23]	Roda F, Musulin E. An ontology-based framework to support intelligent data analysis of sensor measurements[J]. Expert Systems with Applications, 2014, 41(17):7914-7926. Google Scholar
[24]	Chen D Y, Peng L, Li W C, et al. Building extraction and number statistics in WUI areas based on UNet structure and ensemble learning[J]. Remote Sensing, 2021, 13(6):1172. Google Scholar
[25]	Musen M A. The protégé project:A look back and a look forward[J]. AI Matters, 2015, 1(4):4-12. Google Scholar
[26]	胡刘鹏, 高飞, 胡小华. 基于ARCGIS的城镇地形地籍数据库建设方法研究[J]. 测绘, 2011, 34(4): 152-154,158. Google Scholar
[27]	Hu L P, Gao F, Hu X H. Study of urban terrain and cadastral database building based on ARCGIS[J]. Surveying and Mapping, 2011, 34(4):152-154,158. Google Scholar