Extraction of Maritime Road Networks From Large-Scale AIS Data
Extracting road network information including lane boundaries, lane centerlines, junctions and their relationship from AIS data plays an important role in location based services, urban computing and intelligent transportation systems, etc. However, AIS data are large scale, high noisy, the density...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8830397/ |
| _version_ | 1818557635381690368 |
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| author | Guiling Wang Jinlong Meng Yanbo Han |
| author_facet | Guiling Wang Jinlong Meng Yanbo Han |
| author_sort | Guiling Wang |
| collection | DOAJ |
| description | Extracting road network information including lane boundaries, lane centerlines, junctions and their relationship from AIS data plays an important role in location based services, urban computing and intelligent transportation systems, etc. However, AIS data are large scale, high noisy, the density and quality are very uneven in different areas, extracting a whole, continuous and smooth maritime road network with rich information from such data is a challenging problem. To address these issues, this paper proposes an adaptive maritime road network extraction approach that can extract both lane boundaries and centerlines for a large sea area from AIS data. Based on a road network definition including nodes, segments and segment curves, the approach designs parallel grid merging and filtering algorithms to determine if a grided area is inside lane or not. Lane boundaries are smoothed through jagged edge filtering and Simple Moving Average algorithms before centerline extraction. We evaluate our method based on real world AIS data in various area across the world's seas. Experimental results show the advantage of our method beyond the close related work. |
| first_indexed | 2024-12-14T00:02:07Z |
| format | Article |
| id | doaj.art-8266c4201b264b7fb46f8425f0d2cb6c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:02:07Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8266c4201b264b7fb46f8425f0d2cb6c2022-12-21T23:26:16ZengIEEEIEEE Access2169-35362019-01-01712303512304810.1109/ACCESS.2019.29357948830397Extraction of Maritime Road Networks From Large-Scale AIS DataGuiling Wang0https://orcid.org/0000-0002-4659-2019Jinlong Meng1Yanbo Han2Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, ChinaBeijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, ChinaBeijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, ChinaExtracting road network information including lane boundaries, lane centerlines, junctions and their relationship from AIS data plays an important role in location based services, urban computing and intelligent transportation systems, etc. However, AIS data are large scale, high noisy, the density and quality are very uneven in different areas, extracting a whole, continuous and smooth maritime road network with rich information from such data is a challenging problem. To address these issues, this paper proposes an adaptive maritime road network extraction approach that can extract both lane boundaries and centerlines for a large sea area from AIS data. Based on a road network definition including nodes, segments and segment curves, the approach designs parallel grid merging and filtering algorithms to determine if a grided area is inside lane or not. Lane boundaries are smoothed through jagged edge filtering and Simple Moving Average algorithms before centerline extraction. We evaluate our method based on real world AIS data in various area across the world's seas. Experimental results show the advantage of our method beyond the close related work.https://ieeexplore.ieee.org/document/8830397/AIS dataroad networkspatio-temporal data miningtrajectory data miningtrajectory computingvisual analysis |
| spellingShingle | Guiling Wang Jinlong Meng Yanbo Han Extraction of Maritime Road Networks From Large-Scale AIS Data IEEE Access AIS data road network spatio-temporal data mining trajectory data mining trajectory computing visual analysis |
| title | Extraction of Maritime Road Networks From Large-Scale AIS Data |
| title_full | Extraction of Maritime Road Networks From Large-Scale AIS Data |
| title_fullStr | Extraction of Maritime Road Networks From Large-Scale AIS Data |
| title_full_unstemmed | Extraction of Maritime Road Networks From Large-Scale AIS Data |
| title_short | Extraction of Maritime Road Networks From Large-Scale AIS Data |
| title_sort | extraction of maritime road networks from large scale ais data |
| topic | AIS data road network spatio-temporal data mining trajectory data mining trajectory computing visual analysis |
| url | https://ieeexplore.ieee.org/document/8830397/ |
| work_keys_str_mv | AT guilingwang extractionofmaritimeroadnetworksfromlargescaleaisdata AT jinlongmeng extractionofmaritimeroadnetworksfromlargescaleaisdata AT yanbohan extractionofmaritimeroadnetworksfromlargescaleaisdata |