Pipeline for open AIS data with filtering based on vessel class (GeoWildLife)

被引:0
|
作者
Bayer, Mirjam [1 ]
Fry, Tabea [1 ]
Dethlefsen, Soeren [1 ]
Kazempour, Daniyal [1 ]
机构
[1] Univ Kiel, Kiel, Germany
关键词
data set generator; AIS; pipeline; open data;
D O I
10.1145/3615893.3628758
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With declining fish populations, there is a high demand for measures to monitor fishing efforts and promote maritime conservation. Due to advancing technologies, data documenting vessel activity is continuously growing. The generated data and data streams of vessels in the form of satellite-based communication such as Automatic Identification System (AIS) contain valuable information on the activities performed, yet this information is hidden inside the massive volume of data streams. To facilitate research on AIS data, we present a pipeline that extracts useful data sets from standardized AIS message streams. The pipeline is equipped to process any standardized AIS message data stream, as demonstrated on AIS messages of the Danish Marine Authority (DMA). The presented data set generator allows filtering and cleaning available AIS messages; reducing the otherwise massive volume of data and yielding a specific data set for further research like trajectory classification or fishing effort estimation. The implemented filter is based on mandatory features contained in AIS message. These features are: "Navigational status", "Ship Type" and "Cargo Type". The pipeline can output data sets of three different vessel classes given the AIS data, resulting in data sets containing either messages from merchandise ships, fishing vessels, or passenger ships. The data can optionally be cleaned and enriched by additional features such as water depth, distance to shore, or trip and anchorage annotation.
引用
收藏
页码:21 / 24
页数:4
相关论文
共 50 条
  • [1] RFID data filtering model based on AIS
    Wu, Huarui
    Li, Meiying
    Zhao, Chunjiang
    Zhu, Huaji
    Sun, Xiang
    Yang, Baozhu
    PROGRESS OF INFORMATION TECHNOLOGY IN AGRICULTURE, 2007, : 439 - 443
  • [2] Intelligent Vessel Dynamics Video Monitoring System based on AIS Data
    Zhou Jianmin
    Wang Jie
    2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, 2009, : 4932 - +
  • [3] Towards a Model of Regional Vessel Near-miss Collision Risk Assessment for Open Waters based on AIS Data
    Zhang, Weibin
    Feng, Xinyu
    Qi, Yong
    Shu, Feng
    Zhang, Yijin
    Wang, Yinhai
    JOURNAL OF NAVIGATION, 2019, 72 (06): : 1449 - 1468
  • [4] Statistical Approach in Data Filtering for Prediction Vessel Movements Through Time and Estimation Route Using Historical AIS Data
    Bautista-Sanchez, Rogelio
    Ibeth Barbosa-Santillan, Liliana
    Jaime Sanchez-Escobar, Juan
    ADVANCES IN SOFT COMPUTING, MICAI 2019, 2019, 11835 : 28 - 38
  • [5] Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data
    Yan, Ran
    Wang, Shuaian
    SMART TRANSPORTATION SYSTEMS 2019, 2019, 149 : 29 - 37
  • [6] Modelling and simulating vessel emissions in real time based on terrestrial AIS data
    Spiliopoulos, Giannis
    Zissis, Dimitris
    de la Cueva, Julio
    Kontopoulos, Ioannis
    GLOBAL OCEANS 2020: SINGAPORE - U.S. GULF COAST, 2020,
  • [7] Knowledge-Based Vessel Position Prediction using Historical AIS Data
    Mazzarella, Fabio
    Arguedas, Virginia Fernandez
    Vespe, Michele
    2015 WORKSHOP ON SENSOR DATA FUSION - TRENDS, SOLUTIONS, APPLICATIONS (SDF), 2015,
  • [8] Application of fine vessel emission inventory compilation method based on AIS data
    Zhu, Qian-Ru (zhuqr2006@163.com), 1600, Chinese Society for Environmental Sciences (37):
  • [9] Inference of Single Vessel Behaviour with Incomplete Satellite-based AIS Data
    Liu, Changqing
    Chen, Xiaoqian
    JOURNAL OF NAVIGATION, 2013, 66 (06): : 813 - 823
  • [10] Detection of Abnormal Vessel Behaviours Based on AIS Data Features Using HDBSCAN
    Kumar, R. Hari
    Ramanarayanan, C. P.
    Murthy, K. S. R. R. P.
    DEFENCE SCIENCE JOURNAL, 2023, 73 (04) : 445 - 456