An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source semantic information for places and has many geospatial applications in real estate, transportation, urban planning. With the availability different sources, POI conflation technique enriching quality coverage by merging from multiple sources. This study proposes novel end-to-end framework consisting six steps, starting with procurement, schema standardisation, taxonomy mapping, matching, unification, verification. The feasibility proposed was demonstrated case conducted eastern region Singapore, where five sources conflated to form unified dataset. Based on evaluation conducted, resulting dataset found be more comprehensive complete than any alone. Furthermore, approach identifying matches between outperformed all baseline approaches matching accuracy 97.6% an average run time below 3 min when over 12,000 POIs result 8699 unique POIs, thereby demonstrating framework’s scalability large scale implementation dense contexts.