Geographic entity extraction and correction methods based on the 12345 public service hotline

Shengqi Cai; Lujin Hu; Yu Liu; Zhenkai Wang; Senchuan Di

doi:10.1117/12.3048699

27 November 2024 Geographic entity extraction and correction methods based on the 12345 public service hotline

Shengqi Cai, Lujin Hu, Yu Liu, Zhenkai Wang, Senchuan Di

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 1340226 (2024) https://doi.org/10.1117/12.3048699
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

The 12345 public hotline service is one of the primary channels for residents to provide feedback on urban issues and suggestions. Extracting spatial information from this feedback is crucial for government departments to accurately locate various events and explore their spatial characteristics. Traditional methods, such as those based on regular expressions, often struggle with inaccurate extraction of geographic entity features and low recognition efficiency. To address these issues and improve fine-grained geographic entity recognition, a method for extracting and correcting geographic information based on 12345 text data is proposed in this paper. Initially, official data from the 12345 hotline in Sanya City were used as a corpus, and the geographic entities within it were annotated and classified. Subsequently, three geographic entity extraction models were selected for testing, and relevant evaluation metrics were calculated. Finally, a spatial correction method for geographic entities based on semantic matching was proposed. This method utilizes the Jaccard similarity function and spatial correction buffers to establish correction rules, further optimizing the extraction results. The results show that the geographic entity recognition method based on BERT-BiLSTM-CRF achieves a precision of 89.21%, recall of 92.59%, and F1 score of 90.86%. Additionally, after spatial correction, all geographic entities fall within the POI and road network buffer zones, making the recognition results more accurate and practically relevant.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shengqi Cai, Lujin Hu, Yu Liu, Zhenkai Wang, and Senchuan Di "Geographic entity extraction and correction methods based on the 12345 public service hotline", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 1340226 (27 November 2024); https://doi.org/10.1117/12.3048699

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KEYWORDS

Data modeling

Semantics

Performance modeling

Roads

Education and training

Process modeling

Data corrections

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