Multi-source POI location fusion considering address semantics and geospatial features

doi:10.13474/j.cnki.11-2246.2023.0327

Abstract

Abstract: Multi-source POI location fusion is one of the key technologies for geospatial data matching and fusion. However, due to the difference of location coding and location error between different POI data sources, location fusion becomes more difficult. Multi-source POI location fusion considering address semantics and geospatial feature is proposed. Firstly, semantic features of address attributes are extracted by TextRCNN and graph attention network. Then, Multi-layer perceptron is used to extract geospatial features of location attributes. Finally, multi-source POI location fusion is realized by feature aggregation based on self-attention mechanism. We conduct experimental verification on the POI data of Baidu map, Tencent map and Amap in Chengdu. The results show that this method is significantly superior to the existing methods, and the average location fusion accuracy is better than 12 m.

Key words: address semantics, geospatial features, TextRCNN, graph attention network, multi-layer perceptron, self-attention mechanism

CLC Number:

P208

LI Pengpeng, LIU Jiping, WAGN Yong, LUO An, SANG Yu, YAN Xuefeng. Multi-source POI location fusion considering address semantics and geospatial features[J]. Bulletin of Surveying and Mapping, 2023, 0(11): 54-60.

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