Pubblicazione scientifica

Identifying fishing activities from AIS data with Conditional Random Fields

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità
produzione e cattura
sicurezza e qualità sui luoghi di lavoro
trasformazione e commercializzazione

RIASSUNTO

Fishing activity detection is important for fishery management to maintain abundant oceans. This paper presents a novel approach to identifying fishing activities from Automatic Identification System (AIS) data using Conditional Random Fields (CRFs). CRFs are popular for solving structured prediction problems such as sequence labeling in natural language processing. To model the conditional probability distributions that can identify fishing activities of the vessel points, we treat attributes of vessel points as observed variables and the fishing and non-fishing labels as hidden variables. We present three experiments and two comparisons to demonstrate the stability and effectiveness of the resulting models.

DATA

Data di pubblicazione: 01/09/2016

AUTORI

BAIFAN HU
XIANG JIANG
ERICO N DE SOUZA
RONALD PELOT
STAN MATWIN

ENTE DI AFFERENZA

FACULTY OF COMPUTER SCIENCE DALHOUSIE UNIV HALIFAX NS CANADA B3H 1W5

RIVISTA

2016 Federated Conference on Computer Science and Information Systems (FedCSIS) (Page(s): 47-52)

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