期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/ijgi10110779
关键词
data integration; data fusion; data conflation; volunteered geographic information; machine learning; natural language processing
POI data serves as a valuable source of semantic information for places of interest, and POI conflation is an important technique for enriching data quality and coverage. The proposed end-to-end POI conflation framework was successfully demonstrated in a case study in Singapore, showing its feasibility and scalability.
Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework's scalability for large scale implementation in dense urban contexts.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据