Voronoi diagram-based approach to identify maritime corridors

Mariem Masmoudi; Salem Chakhar; Habib Chabchoub

doi:10.1007/978-3-031-30396-8_15

Voronoi diagram-based approach to identify maritime corridors

Mariem Masmoudi^*, Salem Chakhar, Habib Chabchoub

^*Corresponding author for this work

School of Organisations, Systems and People

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Downloads (Pure)

Abstract

This paper proposes a three-phase procedure for maritime corridor generation. The main input of this procedure is a bathymetric map. It outputs a collection of potential corridors relating different start and end points. The proposed procedure is structured into three successive phases: (1) spatial data transformation; (2) construction of the connectivity graph; and (3) identification of potential corridors. The proposed approach has been implemented and applied to identify a collection of corridors for locating a maritime highway linking the archipelago of Kerkennah to Sfax city in Tunisia. Four pairs of start and end points have been considered in this application, leading to four potential corridors, each represented as a collection of linearly adjacent polygons.

Original language	English
Title of host publication	Key Digital Trends in Artificial Intelligence and Robotics
Subtitle of host publication	Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning
Editors	Luigi Troiano, Alfredo Vaccaro, Nishtha Kesswani, Irene Díaz Rodriguez, Imene Brigui, David Pastor-Escuredo
Place of Publication	9783031303951
Publisher	Springer
Pages	167-177
Number of pages	11
ISBN (Electronic)	9783031303968
DOIs	https://doi.org/10.1007/978-3-031-30396-8_15
Publication status	Published - 17 Apr 2023

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	670
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Keywords

Voronoi diagram
shortest path
Dijkstra algorithm
maritime corridors
GIS

Access to Document

10.1007/978-3-031-30396-8_15

Voronoi Diagram-Based Approach to Identify Maritime Corridors
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-30396-8_15.
Accepted author manuscript (Post-print), 1.63 MB

Cite this

Masmoudi, M., Chakhar, S., & Chabchoub, H. (2023). Voronoi diagram-based approach to identify maritime corridors. In L. Troiano, A. Vaccaro, N. Kesswani, I. Díaz Rodriguez, I. Brigui, & D. Pastor-Escuredo (Eds.), Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning (pp. 167-177). (Lecture Notes in Networks and Systems; Vol. 670). Springer. https://doi.org/10.1007/978-3-031-30396-8_15

Masmoudi, Mariem ; Chakhar, Salem ; Chabchoub, Habib. / Voronoi diagram-based approach to identify maritime corridors. Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning. editor / Luigi Troiano ; Alfredo Vaccaro ; Nishtha Kesswani ; Irene Díaz Rodriguez ; Imene Brigui ; David Pastor-Escuredo. 9783031303951 : Springer, 2023. pp. 167-177 (Lecture Notes in Networks and Systems).

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Masmoudi, M, Chakhar, S & Chabchoub, H 2023, Voronoi diagram-based approach to identify maritime corridors. in L Troiano, A Vaccaro, N Kesswani, I Díaz Rodriguez, I Brigui & D Pastor-Escuredo (eds), Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning. Lecture Notes in Networks and Systems, vol. 670, Springer, 9783031303951, pp. 167-177. https://doi.org/10.1007/978-3-031-30396-8_15

Voronoi diagram-based approach to identify maritime corridors. / Masmoudi, Mariem; Chakhar, Salem; Chabchoub, Habib.
Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning. ed. / Luigi Troiano; Alfredo Vaccaro; Nishtha Kesswani; Irene Díaz Rodriguez; Imene Brigui; David Pastor-Escuredo. 9783031303951: Springer, 2023. p. 167-177 (Lecture Notes in Networks and Systems; Vol. 670).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Masmoudi M, Chakhar S, Chabchoub H. Voronoi diagram-based approach to identify maritime corridors. In Troiano L, Vaccaro A, Kesswani N, Díaz Rodriguez I, Brigui I, Pastor-Escuredo D, editors, Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning. 9783031303951: Springer. 2023. p. 167-177. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-30396-8_15