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Article

A Deep Learning Streaming Methodology for Trajectory Classification

1
Department of Informatics and Telematics, Harokopio University of Athens, 9 Omirou Str., 17778 Athens, Greece
2
Department of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., 15773 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Christophe Claramunt and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 250; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040250
Received: 10 March 2021 / Revised: 3 April 2021 / Accepted: 5 April 2021 / Published: 8 April 2021
Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance. View Full-Text
Keywords: trajectory classification; deep learning; neural networks; computer vision; distributed processing; stream processing; real-time vessel monitoring; trajectory compression; AIS trajectory classification; deep learning; neural networks; computer vision; distributed processing; stream processing; real-time vessel monitoring; trajectory compression; AIS
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MDPI and ACS Style

Kontopoulos, I.; Makris, A.; Tserpes, K. A Deep Learning Streaming Methodology for Trajectory Classification. ISPRS Int. J. Geo-Inf. 2021, 10, 250. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040250

AMA Style

Kontopoulos I, Makris A, Tserpes K. A Deep Learning Streaming Methodology for Trajectory Classification. ISPRS International Journal of Geo-Information. 2021; 10(4):250. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040250

Chicago/Turabian Style

Kontopoulos, Ioannis; Makris, Antonios; Tserpes, Konstantinos. 2021. "A Deep Learning Streaming Methodology for Trajectory Classification" ISPRS Int. J. Geo-Inf. 10, no. 4: 250. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040250

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