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U.S. Department of Energy
Office of Scientific and Technical Information
 

Correlating real-world incidents with vessel traffic off the coast of Hawaii, 2017–2020

Journal Article · · Discover Oceans

Abstract Objectives

Because of the high-risk nature of emergencies and illegal activities at sea, it is critical that algorithms designed to detect anomalies from maritime traffic data be robust. However, there exist no publicly available maritime traffic data sets with real-world expert-labeled anomalies. As a result, most anomaly detection algorithms for maritime traffic are validated without ground truth.

Data description

We introduce the HawaiiCoast_GT data set, the first ever publicly available automatic identification system (AIS) data set with a large corresponding set of true anomalous incidents. This data set—cleaned and curated from raw Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration (NOAA) automatic identification system (AIS) data—covers Hawaii’s coastal waters for four years (2017–2020) and contains 88,749,176 AIS points for a total of 2622 unique vessels. This includes 208 labeled tracks corresponding to 154 rigorously documented real-world incidents.

Sponsoring Organization:
USDOE
OSTI ID:
2282184
Alternate ID(s):
OSTI ID: 2311259
Journal Information:
Discover Oceans, Journal Name: Discover Oceans Journal Issue: 1 Vol. 1; ISSN 2948-1562
Publisher:
Springer Science + Business MediaCopyright Statement
Country of Publication:
Switzerland
Language:
English

References (15)

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IAVT: Anomalous Vessel Trajectory Detection Using AIS Data conference May 2022
Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain conference November 2015
A Distributed Spatial Method for Modeling Maritime Routes journal January 2020
Contextual verification for false alarm reduction in maritime anomaly detection conference October 2015
Analysis of Vessel Anomalous Behavior Based on Bayesian Recurrent Neural Network conference April 2020
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Maritime Anomaly Detection for Vessel Traffic Services: A Survey journal June 2023

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