The study of identification of fishing vessel behavior based on VMS data

Feng, Yuan; Zhao, Xuesheng; Han, Mingxu; Sun, Tianying; Li, Chen

doi:10.1145/3369555.3369574

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…Regarding the classification of fishing gear based on satellite data (i.e., the objective of this manuscript), this task has been addressed as well using both VMS [6,[44][45][46][47][48][49] and AIS [7,9,[50][51][52] data samples to extract trajectory information. Marzuki et al [44] proposed to characterize each fishing gear motion by training an independent GMM per gear type, which models both speed and turning angle.…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal trajectory data modeling for fishing gear classification

Rodriguez-Albala,

Peña,

Melzi

et al. 2024

Pattern Anal Applic

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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 $$90\%$$ 90 % . 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 $$99\%$$ 99 % . 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.

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Section: Related Workmentioning

confidence: 99%

Spatio-temporal trajectory data modeling for fishing gear classification

Rodriguez-Albala,

Peña,

Melzi

et al. 2024

Pattern Anal Applic

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“…The results showed that the accuracy of fishing vessel operation-type recognition after feature fusion was significantly improved. Feng et al [38] used VMS data and back propagation (BP) neural networks to identify the fishing behavior of fishing vessels. The change trend in the direction angle and speed of the fishing vessels were selected as the input parameters of the BP neural network model, and the fishing behavior was effectively identified.…”

Section: Identifying Behavioral Characteristics Of Fishing Vesselsmentioning

confidence: 99%

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Cheng,

Zhang,

Chen

et al. 2023

Fishes

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Monitoring and understanding the behavior of fishing vessels are important in facilitating effective management, preventing illegal fishing, informing fishing grounds and evaluating effects of harvests on fishery resources. In recent decades, a large quantity of real-time data of fishing vessels have become available with the development of vessel-tracking systems, making it possible to study the behavior of fishing vessels in high spatial and temporal resolutions. To effectively and efficiently deal with the large amount of data, algorithms from artificial intelligence (AI) are increasingly applied in the study of fishing vessel behavior. In this paper, we first introduce the various data sources for studying fishing vessel behavior and compare their pros and cons. Secondly, we review the AI methods that have been used to monitor and extract the behavior of fishing vessels from big data. Then, studies on the physical, ecological and social mechanisms affecting the behavior of fishing vessels were synthesized. Lastly, we review the applications of fishing vessel behavior in fishery science and management.

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“…The vessel speed was commonly used to evaluate fishing activity from VMS data (Feng et al, 2019;Gerritsen & Lordan, 2011). The cast net operation began with dropping the anchor to make the boat more stable, then falling the open net onto a schooling squid.…”

Section: Vessel Speedmentioning

confidence: 99%

Identification of productive miter squid fishing grounds using a generalized additive model in Indonesian Fisheries Management Area 711

al.¹

2022

Egypt. J. of Aquatic Biolo. and Fish.

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This study aimed to evaluate the factors affecting the catch rate and predict the potential catch for the determination of productive fishing grounds for miter squid (Uroteuthis chinensis) in the Indonesian Fisheries Management Area 711 for 2020. A vessel monitoring system and landing data from 66 cast nets were used to investigate catch per unit effort. Depth, chlorophyll-a, and sea surface temperature from satellite imagery combined with the month and geographic location of fishing were evaluated for their effect on catch per unit effort using a general additive model. The best model output was employed to identify productive fishing grounds. The results revealed that the average monthly catch rate ranged from 89.43-337.47kg h -1 , with the highest occurring in May. Three significant variables affected the catch rate: month, latitude, and sea surface temperature, with contributions of 42.6%, 33.6%, and 12.7%, respectively. The catch rate continued to rise from March to May and tended to increase at higher latitudes where sea surface temperatures ranged from 27.6-29.5°C or 31.5-32.2°C. Based on the monthly map of predicted potential catch, the most productive fishing grounds were located in the waters between the islands of Anambas, Natuna, and Tambelan. These findings can support marine spatial planning and squid resource management in IFMA 711.

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The study of identification of fishing vessel behavior based on VMS data

Cited by 6 publications

References 8 publications

Spatio-temporal trajectory data modeling for fishing gear classification

Spatio-temporal trajectory data modeling for fishing gear classification

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Identification of productive miter squid fishing grounds using a generalized additive model in Indonesian Fisheries Management Area 711

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