气候变化领域集成服务门户(CCPortal): Spatio-temporal trajectory data modeling for fishing gear classification

CCPortal

DOI	10.1007/s10044-024-01263-2
	Spatio-temporal trajectory data modeling for fishing gear classification
	Rodriguez-Albala, Juan Manuel; Pena, Alejandro; Melzi, Pietro; Morales, Aythami; Tolosana, Ruben; Fierrez, Julian; Vera-Rodriguez, Ruben; Ortega-Garcia, Javier
发表日期	2024
ISSN	1433-7541
EISSN	1433-755X
起始页码	27
结束页码	2
卷号	27 期号:2
英文摘要	International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels' fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to 90 % \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$90\%$$\end{document} . Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of 99 % \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99\%$$\end{document} . We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.
英文关键词	Fishing gear classification; Database; Illegal fishing; Spatio-temporal trajectory data modeling; AI for social good; Biodiversity; Climate change
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001204395100001
来源期刊	PATTERN ANALYSIS AND APPLICATIONS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303653
作者单位	Autonomous University of Madrid
推荐引用方式 GB/T 7714	Rodriguez-Albala, Juan Manuel,Pena, Alejandro,Melzi, Pietro,et al. Spatio-temporal trajectory data modeling for fishing gear classification[J],2024,27(2).
APA	Rodriguez-Albala, Juan Manuel.,Pena, Alejandro.,Melzi, Pietro.,Morales, Aythami.,Tolosana, Ruben.,...&Ortega-Garcia, Javier.(2024).Spatio-temporal trajectory data modeling for fishing gear classification.PATTERN ANALYSIS AND APPLICATIONS,27(2).
MLA	Rodriguez-Albala, Juan Manuel,et al."Spatio-temporal trajectory data modeling for fishing gear classification".PATTERN ANALYSIS AND APPLICATIONS 27.2(2024).