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

doi:10.6046/zrzyyg.2022022

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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.

Keywords spatio-temporal knowledge graph cadastral data cadastral database

ZTFLH:

TP79

Issue Date: 20 March 2023

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	Luanjie CHEN
	Weichao LI
	Ling PENG
	Jiahui CHEN
	Xiang GAO

Cite this article:

Luanjie CHEN,Weichao LI,Ling PENG, et al. A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs[J]. Remote Sensing for Natural Resources, 2023, 35(1): 243-250.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022022 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/243

Fig.1 Business logic of cadastral data quality inspection and update

Tab.1 Types of cadastral data errors and quality inspection methods

Fig.2 Flow chart of the quality inspection and updating of cadastral data

Fig.3 Spatio-temporal knowledge graphs architecture for the quality inspection and updating of cadastral data

Fig.4 Logic structure diagram of SWRLTO time ontology

Fig.5 Logical structure diagram of GeoSPARQL spatial ontology

Fig.6 Rule concept logic structure diagram

Fig.7 Storage structure of cadastral data source

Fig.8 Process diagram of knowledge extraction for cadastral data

Fig.9 Process diagram of knowledge extraction for remote sensing images

Fig.10 Description of geographical coordinates of triple record

Fig.11 Flow chart of quality inspection reasoning calculation of cadastral data

Tab.2 Efficient analysis of spatio-temporal knowledge graph

Tab.3 Description of experimental parcel attributes and topology

Fig.12 Spatial relationship between parcel D and parcel E

Fig.13 Operation results of unqualified quality inspection of parcel E topology

Fig.14 Operation results of parcel G unqualified in authenticity inspection

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