Document of bibliographic reference 363808

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

BATMAN: A Brain-like approach for tracking maritime activity and nuance

Abstract

As commercial geospatial intelligence data becomes more widely available, algorithms using artificial intelligence need to be created to analyze it. Maritime traffic is annually increasing in volume, and with it the number of anomalous events that might be of interest to law enforcement agencies, governments, and militaries. This work proposes a data fusion pipeline that uses a mixture of artificial intelligence and traditional algorithms to identify ships at sea and classify their behavior. A fusion process of visual spectrum satellite imagery and automatic identification system (AIS) data was used to identify ships. Further, this fused data was further integrated with additional information about the ship’s environment to help classify each ship’s behavior to a meaningful degree. This type of contextual information included things such as exclusive economic zone boundaries, locations of pipelines and undersea cables, and the local weather. Behaviors such as illegal fishing, trans-shipment, and spoofing are identified by the framework using freely or cheaply accessible data from places such as Google Earth, the United States Coast Guard, etc. The pipeline is the first of its kind to go beyond the typical ship identification process to help aid analysts in identifying tangible behaviors and reducing the human workload.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000947474600001

Bibliographic citation

Jones, A.; Koehler, S.; Jerge, M.; Graves, M.; King, B.; Dalrymple, R.; Freese, C.; Von Albade, J. (2023). BATMAN: A Brain-like approach for tracking maritime activity and nuance. Sensors 23(5): 2424. https://dx.doi.org/10.3390/s23052424

Topic

Marine

Is peer reviewed

true

Access rights

open access

Is accessible for free

true