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Dataset . 2021
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
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Reconstrucción de tráfico marítimo a partir de datos AIS incompletos mediante Deep Learning

Authors: Félix Enguix, Iván; Martínez Álvarez-Castellanos, Rosa; López García, Celia;

Reconstrucción de tráfico marítimo a partir de datos AIS incompletos mediante Deep Learning

Abstract

{"references": ["Marine Cadastre: https://marinecadastre.gov/ais", "F. Mazzarella, V. Fernandez Arguedas y M. Vespe, \u00abKnowledge-based vessel position prediction using historical AIS data,\u00bb 2015 Sensor Data Fusion: Trends, Solutions, Applications (SDF), pp. 1-6, 2015", "Alvaro Orgaz Exp\u00f3sito - Deep Learning & Graph clustering for Maritime Logistics - Predicting destination and Expected time of Arrival for vessels across Europe (2020)"]}

Reconstrucción de rutas marítimas a partir de datos AIS incompletos. Se presenta la predicción del tráfico marítimo a partir de estos datos mediante algoritmos de aprendizaje profundo. Estos datos se han recopilado para el proyecto BitBlue: Investigación de técnicas de difusión, procesado y análisis de Big Data del medio marino, financiado por el Instituto de Fomento de la Región de Murcia con el apoyo de los Fondos FEDER.

Keywords

AIS, deep learning, tráfico marítimo, reconstrucción de rutas

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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