Architecture for trajectory-based fishing ships 3 classification with AIS data
Articles
Overview
published in
- Sensors Journal
publication date
- July 2020
start page
- 1
end page
- 21
issue
- 13
volume
- 20
Digital Object Identifier (DOI)
full text
International Standard Serial Number (ISSN)
- 1424-3210
Electronic International Standard Serial Number (EISSN)
- 1424-8220
abstract
- This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.
Classification
subjects
- Computer Science
keywords
- ais data; spatiotemporal data mining; data fusion; machine learning; trajectory classification; class imbalance; real-world data