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