Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Marine Science and Engineering Année : 2021

Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation

Résumé

Originally designed as a mode of telecommunication, the network of French sémaphore is now dedicated to the continuous monitoring and recording of marine traffic along the entire French coast. Although the observation data collected by sémaphores cover 7/7 days and 24/24 h and could provide precious information regarding marine traffic, they remain underexploited. Indeed, these data concern all types of traffic, including leisure boating and smaller craft that are not usually recorded by the most common means of observation, such as AIS, radar and satellite. Based on sémaphore data, traffic pressure and its spatiotemporal distribution can be fully measured to better analyze its interactions with human activities and the environment. One drawback of these data is their initially semantic nature, which requires the development of an original processing method. The protocol developed to analyze the marine traffic of the Iroise Sea and its first results are presented in this article. It is based on a semi-automatic method aimed to clean the original data and quantify the marine traffic along synthetic routes. It includes a procedure that takes into account the temporal evolution of the traffic based on the Allen’s time framework. The results proved interesting as they provide an overview of marine traffic, including all types of vessels, and may be defined for different time periods and granularity. A description of the numerical and geographic instruments created is given; all the written code is released as Open Source software and freely available for download and testing.

Domaines

Géographie Autre
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Dates et versions

hal-03163626 , version 1 (28-02-2022)

Identifiants

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Annalisa Minelli, Iwan Le Berre, Ingrid I. Peuziat, Mathias Rouan. Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation. Journal of Marine Science and Engineering, 2021, 9 (3), pp.294. ⟨10.3390/jmse9030294⟩. ⟨hal-03163626⟩
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