Conflation of Geospatial POI Data and Ground-level Imagery via Link Prediction on Joint Semantic Graph

Conference · Mon Nov 01 00:00:00 EDT 2021

DOI:https://doi.org/10.1145/3486635.3491068· OSTI ID:1870246

Gurav, Rutuja ^[1]; De, Debraj ^[1]; Thakur, Gautam Malviya  ^[1]; Fan, Junchuan  ^[1]

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1. ORNL

With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1870246
• Resource Relation: Conference: The 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GEOAI '21) - Online / Beijing, China, , China - 11/2/2021 8:00:00 AM-11/5/2021 8:00:00 AM
• Country of Publication: United States
• Language: English