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T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

Abstract

Maritime road network is composed of detailed maritime routes and is vital in many applications such as threats detection, traffic control. However, the vessel trajectory data, or Automatic Identification System (AIS) data, are usually large in scale and collected with different sampling rates. And, what’s more, it is difficult to obtain enough accurate road networks as paired training datasets. It is a huge challenge to extract a complete maritime road network from such data that matches the actual route of the ship. In order to solve these problems, this paper proposes an unsupervised learning-based maritime road network extraction model T2I-CycleGAN based on CycleGAN. The method translates trajectory data into unpaired input samples for model training, and adds dense layer to the CycleGAN model to handle trajectories with different sampling rates. We evaluate the approach on real-world AIS datasets in various areas and compare the extracted results with the real ship coordinate data in terms of connectivity and details, achieving effectiveness beyond the most related work.

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References

  1. Agamennoni, G., Nieto, J.I., Nebot, E.M.: Robust inference of principal road paths for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 12(1), 298–308 (2011)

    Article  Google Scholar 

  2. Ahmed, M., Karagiorgou, S., Pfoser, D., Wenk, C.: A comparison and evaluation of map construction algorithms using vehicle tracking data. GeoInformatica 19(3), 601–632 (2015). https://doi.org/10.1007/s10707-014-0222-6

    Article  MATH  Google Scholar 

  3. Arguedas, V.F., Pallotta, G., Vespe, M.: Automatic generation of geographical networks for maritime traffic surveillance. In: 17th International Conference on Information Fusion (FUSION), pp. 1–8 (2014)

    Google Scholar 

  4. Cao, L., Krumm, J.: From GPS traces to a routable road map. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 3–12 (2009)

    Google Scholar 

  5. Chen, C., Cheng, Y.: Roads digital map generation with multi-track GPS data. In: 2008 International Workshop on Geoscience and Remote Sensing, vol. 1, pp. 508–511 (2008)

    Google Scholar 

  6. Chen, C., Lu, C., Huang, Q., Yang, Q., Gunopulos, D., Guibas, L.: City-scale map creation and updating using GPS collections. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1465–1474. ACM, New York (2016)

    Google Scholar 

  7. Chuanwei, L., Qun, S., Bing, C., Bowei, W., Yunpeng, Z., Li, X.: Road learning extraction method based on vehicle trajectory data. Acta Geodaetica et Cartographica Sinica 49(6), 692 (2020)

    Google Scholar 

  8. Dobrkovic, A., Iacob, M.E., van Hillegersberg, J.: Maritime pattern extraction and route reconstruction from incomplete AIS data. Int. J. Data Sci. Anal. 5(2), 111–136 (2018). https://doi.org/10.1007/s41060-017-0092-8

    Article  Google Scholar 

  9. Ducruet, C., Notteboom, T.: The worldwide maritime network of container shipping: spatial structure and regional dynamics. Glob. Netw. 12(3), 395–423 (2012)

    Article  Google Scholar 

  10. Engin, D., Genç, A., Kemal Ekenel, H.: Cycle-Dehaze: enhanced CycleGAN for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 825–833 (2018)

    Google Scholar 

  11. Etienne, L., Devogele, T., Bouju, A.: Spatio-temporal trajectory analysis of mobile objects following the same itinerary. Adv. Geo-Spatial Inf. Sci. 10, 47–57 (2012)

    Google Scholar 

  12. Fernandez Arguedas, V., Pallotta, G., Vespe, M.: Maritime traffic networks: from historical positioning data to unsupervised maritime traffic monitoring. IEEE Trans. Intell. Transp. Syst. 19(3), 722–732 (2018)

    Article  Google Scholar 

  13. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Hung, C.C., Peng, W.C., Lee, W.C.: Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J. 24(2), 169–192 (2015). https://doi.org/10.1007/s00778-011-0262-6

    Article  Google Scholar 

  16. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

    Google Scholar 

  17. Le Guillarme, N., Lerouvreur, X.: Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness. In: Proceedings of the 16th International Conference on Information Fusion, pp. 2025–2032 (2013)

    Google Scholar 

  18. Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, pp. 593–604. ACM, New York (2007)

    Google Scholar 

  19. Li, J., Chen, W., Li, M., Zhang, K., Yajun, L.: The algorithm of ship rule path extraction based on the grid heat value. J. Comput. Res. Dev. 55(5), 908–919 (2018)

    Google Scholar 

  20. Liu, X., Biagioni, J., Eriksson, J., Wang, Y., Forman, G., Zhu, Y.: Mining large-scale, sparse GPS traces for map inference: comparison of approaches. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 669–677. ACM, New York (2012)

    Google Scholar 

  21. Lu, Y., Tai, Y.-W., Tang, C.-K.: Attribute-guided face generation using conditional CycleGAN. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 293–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_18

    Chapter  Google Scholar 

  22. Naserian, E., Wang, X., Dahal, K., Wang, Z., Wang, Z.: Personalized location prediction for group travellers from spatial-temporal trajectories. Future Gener. Comput. Syst. 83, 278–292 (2018)

    Article  Google Scholar 

  23. Pallotta, G., Vespe, M., Bryan, K.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(6), 2218–2245 (2013)

    Article  Google Scholar 

  24. Wen, R., Yan, W., Zhang, A.N., Chinh, N.Q., Akcan, O.: Spatio-temporal route mining and visualization for busy waterways. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 849–854 (2016)

    Google Scholar 

  25. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  26. Ruan, S., et al.: Learning to generate maps from trajectories. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 890–897 (2020)

    Google Scholar 

  27. Shi, W., Shen, S., Liu, Y.: Automatic generation of road network map from massive GPS, vehicle trajectories. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6 (2009)

    Google Scholar 

  28. Spiliopoulos, G., Zissis, D., Chatzikokolakis, K.: A big data driven approach to extracting global trade patterns. In: Doulkeridis, C., Vouros, G.A., Qu, Q., Wang, S. (eds.) MATES 2017. LNCS, vol. 10731, pp. 109–121. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73521-4_7

    Chapter  Google Scholar 

  29. Stanojevic, R., Abbar, S., Thirumuruganathan, S., Chawla, S., Filali, F., Aleimat, A.: Robust road map inference through network alignment of trajectories. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 135–143. SIAM (2018)

    Google Scholar 

  30. Tang, L., Ren, C., Liu, Z., Li, Q.: A road map refinement method using Delaunay triangulation for big trace data. ISPRS Int. J. Geo-Inf. 6(2), 45 (2017)

    Article  Google Scholar 

  31. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)

  32. Wang, G., et al.: Adaptive extraction and refinement of marine lanes from crowdsourced trajectory data. Mobile Netw. Appl. 25, 1392–1404 (2020). https://doi.org/10.1007/s11036-019-01454-w

    Article  Google Scholar 

  33. Wang, S., Wang, Y., Li, Y.: Efficient map reconstruction and augmentation via topological methods. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2015)

    Google Scholar 

  34. Wei, Y., Tinghua, A.: Road centerline extraction from crowdsourcing trajectory data. Geogr. Geo Inf. Sci. 32(3), 1–7 (2016)

    Google Scholar 

  35. Yan, W., Wen, R., Zhang, A.N., Yang, D.: Vessel movement analysis and pattern discovery using density-based clustering approach. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3798–3806 (2016)

    Google Scholar 

  36. Yang, W., Ai, T.: The extraction of road boundary from crowdsourcing trajectory using constrained Delaunay triangulation. Acta Geodaetica Cartogr. Sin. 46(2), 237–245 (2017)

    Google Scholar 

  37. Yang, W., Ai, T., Lu, W.: A method for extracting road boundary information from crowdsourcing vehicle GPS trajectories. Sensors 18(4), 2660–2680 (2018)

    Google Scholar 

  38. Zhao, S., et al.: CycleEmotionGAN: emotional semantic consistency preserved cyclegan for adapting image emotions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 2620–2627 (2019)

    Google Scholar 

  39. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 61832004 and Grant 61672042. We thank the Ocean Information Technology Company, China Electronics Technology Group Corporation (CETC Ocean Corp.), for providing the underlying dataset for this research.

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100144, China

    Xuankai Yang, Guiling Wang, Jiahao Yan & Jing Gao

  2. Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, China

    Xuankai Yang, Guiling Wang, Jiahao Yan & Jing Gao

Authors
  1. Xuankai Yang
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  2. Guiling Wang
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  3. Jiahao Yan
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  4. Jing Gao
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Corresponding author

Correspondence to Guiling Wang .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Yang, X., Wang, G., Yan, J., Gao, J. (2021). T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-67540-0_12

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  • Online ISBN: 978-3-030-67540-0

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