research-article

Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange

Authors:

Guillaume Touya,

Imran LokhatAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 6, Issue 3

Article No.: 21, Pages 1 - 21

https://doi.org/10.1145/3382080

Published: 15 April 2020 Publication History

Abstract

Spatial analysis and pattern recognition with vector spatial data is particularly useful to enrich raw data. In road networks, for instance, there are many patterns and structures that are implicit with only road line features, among which highway interchange appeared very complex to recognize with vector-based techniques. The goal is to find the roads that belong to an interchange, such as the slip roads and the highway roads connected to the slip roads. To go further than state-of-the-art vector-based techniques, this article proposes to use raster-based deep learning techniques to recognize highway interchanges. The contribution of this work is to study how to optimally convert vector data into small images suitable for state-of-the-art deep learning models. Image classification with a convolutional neural network (i.e., is there an interchange in this image or not?) and image segmentation with a u-net (i.e., find the pixels that cover the interchange) are experimented and give better results than existing vector-based techniques in this specific use case (99.5% against 74%).

References

[1]

L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2017. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 4 (2017), 834--848. http://arxiv.org/abs/1606.00915.

[2]

Nicolas Audebert, Bertrand Le Saux, and Sebastien Lefevre. 2017. Joint learning from earth observation and OpenStreetMap data to get faster better semantic maps. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’17). IEEE, Los Alamitos, CA, 1552--1560.

[3]

R. F. Berriel, A. T. Lopes, A. F. de Souza, and T. Oliveira-Santos. 2017. Deep learning-based large-scale automatic satellite crosswalk classification. IEEE Geoscience and Remote Sensing Letters 14, 9 (Sept. 2017), 1513--1517.

[4]

Jiaoyan Chen and Alexander Zipf. 2017. DeepVGI: Deep learning with volunteered geographic information. In Proceedings of the 26th International Conference on World Wide Web Companion (WWW Companion’17). ACM, New York, NY, 771--772.

Digital Library

[5]

Ahmet Ozgur Dogru, Nico Van de Weghe, Necla Ulugtekin, and Philippe De Maeyer. 2007. Classification of road junctions based on multiple representations: Adding value by introducing algorithmic and cartographic approaches. In Proceedings of the International Cartographic Conference.

[6]

Birgit Elias. 2003. Extracting landmarks with data mining methods. In Spatial Information Theory: Foundations of Geographic Information Science. Lecture Notes in Computer Science, Vol. 2825. Springer, 375--389.

[7]

Frauke Heinzle and Karl-Heinrich Anders. 2007. Characterising space via pattern recognition techniques: Identifying patterns in road networks. In The Generalisation of Geographic Information: Models and Applications, W. A. Mackaness, A. Ruas, and T. Sarjakoski (Eds.). Elsevier, 233--253.

[8]

Frauke Heinzle, Karl-Heinrich Anders, and Monika Sester. 2005. Graph based approaches for recognition of patterns and implicit information in road networks. In Proceedings of the International Cartographic Conference (ICA’05).

[9]

Z. Huang, G. Cheng, H. Wang, H. Li, L. Shi, and C. Pan. 2016. Building extraction from multi-source remote sensing images via deep deconvolution neural networks. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’16). 1835--1838.

[10]

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). http://arxiv.org/abs/1611.07004

[11]

Yuhao Kang, Song Gao, and Robert E. Roth. 2019. Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography 5, 2--3 (2019), 115--141.

[12]

Loïc Landrieu and Martin Simonovsky. 2018. Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’18).

[13]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (Nov. 1998), 2278--2324.

[14]

William A. Mackaness and Geoffrey Edwards. 2002. The importance of modelling pattern and structure in automated map generalisation. In Proceedings of the Joint ISPRS/ICA Workshop on Multi-Scale Representations of Spatial Data. 7--8. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.1311.

[15]

William A. Mackaness and Gordon A. Mackechnie. 1999. Automating the detection and simplification of junctions in road networks. Geoinformatica 3, 2 (1999), 185--200.

Digital Library

[16]

Y. Méneroux, V. Dizier, M. Margollé, M. D. Van Damme, Y. Kato, A. Le Guilcher, and Guillaume Saint-Pierre. 2018. Convolutional neural network for road sign inference based on GPS traces. In Spatial Big Data and Machine Learning in GIScienceWorkshop at GIScience 2018, Melbourne. Leibniz International Proceedings in Informatics. Schloss Dagstuhl, Leibniz-Zentrum fur Informatik, Dagstuhl Publishing, Germany, 4.

[17]

Torben Peters and Claus Brenner. 2018. Conditional adversarial networks for multimodal photo-realistic point cloud rendering. In Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience 2018, Melbourne. Leibniz International Proceedings in Informatics. Schloss Dagstuhl, Leibniz-Zentrum fur Informatik, Dagstuhl Publishing, Germany, 48--53. http://spatialbigdata.ethz.ch/wp-content/uploads/2018/07/12_Peters_Conditional-Adversarial-Networks-for-Multimodal-Photo-Realistic-Point-Cloud-Rendering-revised.pdf.

[18]

Tristan Postadjian, Arnaud Le Bris, Hichem Sahbi, and Clément Mallet. 2018. Domain adaptation for large scale classification of very high resolution satellite images with deep convolutional neural networks. In Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS’18). 3623--3626.

[19]

Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). arxiv:1506.02640 http://arxiv.org/abs/1506.02640

[20]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. arXiv:1505.04597.

[21]

Sandro Savino, M. Rumor, M. Zanon, and I. Lissandron. 2010. Data enrichment for road generalization through analysis of morphology in the CARGEN project. In Proceedings of 13th ICA Workshop on Generalisation and Multiple Representation.

[22]

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2016. Grad-CAM: Visual explanations from deep networks via gradient-based localization. arXiv:1610.02391.

[23]

M. Sester, Y. Feng, and F. Thiemann. 2018. Building generalization using deep learning. In ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4. Leibniz Universitat Hannover, 565--572.

[24]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15 (2014), 1929--1958. http://jmlr.org/papers/v15/srivastava14a.html.

Digital Library

[25]

Stuart Thom. 2005. A strategy for collapsing OS integrated transport network dual carriageways. In Proceedings of the 8th ICA Workshop on Generalisation and Multiple Representation. http://aci.ign.fr/Acoruna/Papers/Thom.pdf.

[26]

Robert C. Thomson. 2006. The ‘stroke’ concept in geographic network generalization and analysis. In Progress in Spatial Data Handling, A. Riedl, W. Kainz, and G. A. Elmes (Eds.). Springer, 681--697.

[27]

Guillaume Touya. 2010. A road network selection process based on data enrichment and structure detection. Transactions in GIS 14, 5 (2010), 595--614.

[28]

Guillaume Touya and Marion Dumont. 2017. Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas. In Proceedings of 20th ICA Workshop on Generalisation and Multiple Representation.

[29]

Guillaume Touya, Xiang Zhang, and Imran Lokhat. 2019. Is deep learning the new agent for map generalization? International Journal of Cartography 5, 2--3 (2019), 142--157.

[30]

Yongyang Xu, Zhanlong Chen, Zhong Xie, and Liang Wu. 2017. Quality assessment of building footprint data using a deep autoencoder network. International Journal of Geographical Information Science 31, 10 (Oct. 2017), 1929--1951.

Digital Library

[31]

Bisheng Yang, Xuechen Luan, and Qingquan Li. 2010. An adaptive method for identifying the spatial patterns in road networks. Computers, Environment and Urban Systems 34, 1 (Jan. 2010), 40--48.

[32]

Q. Zhang. 2004. Modelling structure and patterns in road network generalization. In Proceedings of the ICA Workshop on Generalisation and Multiple Representation. http://aci.ign.fr/Leicester/paper/Zhang-v2-ICAWorkshop.pdf.

[33]

Z. Zhang, Q. Liu, and Y. Wang. 2018. Road extraction by deep residual U-net. IEEE Geoscience and Remote Sensing Letters 15, 5 (May 2018), 749--753.

Cited By

Wang AWu FQiu YGong XZhai RLiu CYin J(2024)A detection method for road network interchanges with the MeshCNN based on Delaunay triangulationInternational Journal of Digital Earth10.1080/17538947.2024.235612317:1Online publication date: 4-Jun-2024
https://doi.org/10.1080/17538947.2024.2356123
Harrie LTouya GOucheikh RAi TCourtial ARichter K(2024)Machine learning in cartographyCartography and Geographic Information Science10.1080/15230406.2023.229594851:1(1-19)Online publication date: 19-Feb-2024
https://doi.org/10.1080/15230406.2023.2295948
Kang YGao SRoth R(2024)Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethicsCartography and Geographic Information Science10.1080/15230406.2023.2295943(1-32)Online publication date: 16-Jan-2024
https://doi.org/10.1080/15230406.2023.2295943
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Index Terms

Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 6, Issue 3

Special Issue on Deep Learning for Spatial Algorithms and Systems

September 2020

171 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3394669

Editor:
Walid G. Aref
Purdue University, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 April 2020

Accepted: 01 February 2020

Revised: 01 August 2019

Received: 01 May 2019

Published in TSAS Volume 6, Issue 3

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Cited By

Wang AWu FQiu YGong XZhai RLiu CYin J(2024)A detection method for road network interchanges with the MeshCNN based on Delaunay triangulationInternational Journal of Digital Earth10.1080/17538947.2024.235612317:1Online publication date: 4-Jun-2024
https://doi.org/10.1080/17538947.2024.2356123
Harrie LTouya GOucheikh RAi TCourtial ARichter K(2024)Machine learning in cartographyCartography and Geographic Information Science10.1080/15230406.2023.229594851:1(1-19)Online publication date: 19-Feb-2024
https://doi.org/10.1080/15230406.2023.2295948
Kang YGao SRoth R(2024)Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethicsCartography and Geographic Information Science10.1080/15230406.2023.2295943(1-32)Online publication date: 16-Jan-2024
https://doi.org/10.1080/15230406.2023.2295943
Niu XQian HWang XXie LCui L(2024)Determining the optimal generalization operators for building footprints using an improved graph neural network modelGeocarto International10.1080/10106049.2024.230626539:1Online publication date: 30-Jan-2024
https://doi.org/10.1080/10106049.2024.2306265
Taipalus T(2024)Vector database management systems: Fundamental concepts, use-cases, and current challengesCognitive Systems Research10.1016/j.cogsys.2024.10121685(101216)Online publication date: Jun-2024
https://doi.org/10.1016/j.cogsys.2024.101216
Xu XLiu PGuo M(2023)Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural NetworkISPRS International Journal of Geo-Information10.3390/ijgi1207025312:7(253)Online publication date: 21-Jun-2023
https://doi.org/10.3390/ijgi12070253
Kukreja SKumar TBharate VPurohit ADasgupta AGuha D(2023)Vector Databases and Vector Embeddings-Review2023 International Workshop on Artificial Intelligence and Image Processing (IWAIIP)10.1109/IWAIIP58158.2023.10462847(231-236)Online publication date: 1-Dec-2023
https://doi.org/10.1109/IWAIIP58158.2023.10462847
Touya GPotié QMackaness W(2023)Incorporating ideas of structure and meaning in interactive multi scale mapping environmentsInternational Journal of Cartography10.1080/23729333.2023.22159609:2(342-372)Online publication date: 1-Jun-2023
https://doi.org/10.1080/23729333.2023.2215960
Knura M(2023)Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposesCartography and Geographic Information Science10.1080/15230406.2023.227339751:1(146-167)Online publication date: 21-Nov-2023
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Courtial ATouya GZhang X(2023)DeepMapScaler: a workflow of deep neural networks for the generation of generalised mapsCartography and Geographic Information Science10.1080/15230406.2023.226741951:1(41-59)Online publication date: 26-Oct-2023
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