Text Geolocation Prediction via Self-Supervised Learning
Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/4/170 |
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| author | Yuxing Wu Zhuang Zeng Kaiyue Liu Zhouzheng Xu Yaqin Ye Shunping Zhou Huangbao Yao Shengwen Li |
| author_facet | Yuxing Wu Zhuang Zeng Kaiyue Liu Zhouzheng Xu Yaqin Ye Shunping Zhou Huangbao Yao Shengwen Li |
| author_sort | Yuxing Wu |
| collection | DOAJ |
| description | Text geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. Specifically, GeoSG integrates spatial distance and hierarchical constraints to characterize the interactions of POIs and text in a geographic relationship graph. And it designs two self-supervised tasks to train a shared network to learn the relationships among POIs and texts. Finally, the text geolocations are inferred based on the trained shared network. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art baselines and is robust. This study provides a methodological reference for geolocating various text documents and offers a solution for numerous geographic intelligence tasks that lack labeled samples. |
| format | Article |
| id | doaj-art-b804da89f3724a0baf44c5a3f0c11f40 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-b804da89f3724a0baf44c5a3f0c11f402025-08-20T02:28:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114417010.3390/ijgi14040170Text Geolocation Prediction via Self-Supervised LearningYuxing Wu0Zhuang Zeng1Kaiyue Liu2Zhouzheng Xu3Yaqin Ye4Shunping Zhou5Huangbao Yao6Shengwen Li7School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, McGill University, Montréal, QC H3A 0E9, CanadaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaText geolocation prediction aims to infer the geographic location of text with text semantics, serving as a fundamental task for various geographic applications. As the mainstream approach, the deep learning-based methods follow the supervised learning paradigms, which rely heavily on a large amount of labeled samples to train model parameters. To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. Specifically, GeoSG integrates spatial distance and hierarchical constraints to characterize the interactions of POIs and text in a geographic relationship graph. And it designs two self-supervised tasks to train a shared network to learn the relationships among POIs and texts. Finally, the text geolocations are inferred based on the trained shared network. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art baselines and is robust. This study provides a methodological reference for geolocating various text documents and offers a solution for numerous geographic intelligence tasks that lack labeled samples.https://www.mdpi.com/2220-9964/14/4/170text geolocationself-supervised learninggraph neural networksdata-scarce scenarios |
| spellingShingle | Yuxing Wu Zhuang Zeng Kaiyue Liu Zhouzheng Xu Yaqin Ye Shunping Zhou Huangbao Yao Shengwen Li Text Geolocation Prediction via Self-Supervised Learning ISPRS International Journal of Geo-Information text geolocation self-supervised learning graph neural networks data-scarce scenarios |
| title | Text Geolocation Prediction via Self-Supervised Learning |
| title_full | Text Geolocation Prediction via Self-Supervised Learning |
| title_fullStr | Text Geolocation Prediction via Self-Supervised Learning |
| title_full_unstemmed | Text Geolocation Prediction via Self-Supervised Learning |
| title_short | Text Geolocation Prediction via Self-Supervised Learning |
| title_sort | text geolocation prediction via self supervised learning |
| topic | text geolocation self-supervised learning graph neural networks data-scarce scenarios |
| url | https://www.mdpi.com/2220-9964/14/4/170 |
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