Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery
GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/10/502 |
| _version_ | 1797472926039015424 |
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| author | Jianxin Qin Wenjie Yang Tao Wu Bin He Longgang Xiang |
| author_facet | Jianxin Qin Wenjie Yang Tao Wu Bin He Longgang Xiang |
| author_sort | Jianxin Qin |
| collection | DOAJ |
| description | GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method. |
| first_indexed | 2024-03-09T20:08:02Z |
| format | Article |
| id | doaj.art-aa09df8500154ba78a74c1ad118f841a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T20:08:02Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-aa09df8500154ba78a74c1ad118f841a2023-11-24T00:27:10ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-09-01111050210.3390/ijgi11100502Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing ImageryJianxin Qin0Wenjie Yang1Tao Wu2Bin He3Longgang Xiang4Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, ChinaState Key Laboratory of LIESMARS, Wuhan University, Wuhan 430079, ChinaGPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method.https://www.mdpi.com/2220-9964/11/10/502road networktrajectory dataUAV remote sensing imagerydeep learning |
| spellingShingle | Jianxin Qin Wenjie Yang Tao Wu Bin He Longgang Xiang Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery ISPRS International Journal of Geo-Information road network trajectory data UAV remote sensing imagery deep learning |
| title | Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery |
| title_full | Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery |
| title_fullStr | Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery |
| title_full_unstemmed | Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery |
| title_short | Incremental Road Network Update Method with Trajectory Data and UAV Remote Sensing Imagery |
| title_sort | incremental road network update method with trajectory data and uav remote sensing imagery |
| topic | road network trajectory data UAV remote sensing imagery deep learning |
| url | https://www.mdpi.com/2220-9964/11/10/502 |
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