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

Rutuja Gurav, Debraj De, Gautam S. Thakur, Junchuan Fan
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引用次数: 3

Abstract

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.
基于联合语义图链接预测的地理空间POI数据与地面图像合并
随着智能手机摄像头和社交网络的普及,我们拥有丰富的、多模式的兴趣点(poi)数据,比如文化地标、机构、企业等,这些数据都在特定的兴趣区域(AOI)内(例如,一个县、一个城市或一个社区)提供给我们。跨多种数据源模式的数据合并是维护地理信息系统(GIS)的关键挑战之一。鉴于来自九个不同来源的POI数据,以及来自两个不同图像托管平台的地面地理标记和场景字幕图像,在这项工作中,我们探索了图神经网络(gnn)在利用地理空间数据中明显的自然图结构的同时执行数据合并的应用。初步结果表明,GNN操作能够学习实体(poi和图像)特征的分布,并结合语义最近邻图中实体局部邻域的拓扑结构,从而预测一对实体之间的链接。
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