A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs
CHEN Luanjie1,2(), LI Weichao1, PENG Ling1,2(), CHEN Jiahui1,2, GAO Xiang3
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
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.
陈栾杰, 李玮超, 彭玲, 陈嘉辉, 高翔. 基于时空知识图谱的地籍数据质检与更新方法研究[J]. 自然资源遥感, 2023, 35(1): 243-250.
CHEN Luanjie, LI Weichao, PENG Ling, CHEN Jiahui, GAO Xiang. A method for the quality inspection and update of cadastral data based on spatio-temporal knowledge graphs. Remote Sensing for Natural Resources, 2023, 35(1): 243-250.
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2023, Vol. 35 
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