The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 949–952, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-949-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 949–952, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-949-2015

  30 Apr 2015

30 Apr 2015

LONG-TERM MARINE TRAFFIC MONITORING FOR ENVIRONMENTAL SAFETY IN THE AEGEAN SEA

T. Giannakopoulos1, S. Gyftakis1, E. Charou1, S. Perantonis1, Z. Nivolianitou2, I. Koromila2, and A. Makrygiorgos1 T. Giannakopoulos et al.
  • 1Institute of Informatics and Telecommunications National Centre for Scientific Research "DEMOKRITOS", Greece
  • 2Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre for Scientific Research "DEMOKRITOS", Greece

Keywords: Marine traffic monitoring, spatiotemporal analysis, statistics, policy recommendation

Abstract. The Aegean Sea is characterized by an extremely high marine safety risk, mainly due to the significant increase of the traffic of tankers from and to the Black Sea that pass through narrow straits formed by the 1600 Greek islands. Reducing the risk of a ship accident is therefore vital to all socio-economic and environmental sectors. This paper presents an online long-term marine traffic monitoring work-flow that focuses on extracting aggregated vessel risks using spatiotemporal analysis of multilayer information: vessel trajectories, vessel data, meteorological data, bathymetric / hydrographic data as well as information regarding environmentally important areas (e.g. protected high-risk areas, etc.). A web interface that enables user-friendly spatiotemporal queries is implemented at the frontend, while a series of data mining functionalities extracts aggregated statistics regarding: (a) marine risks and accident probabilities for particular areas (b) trajectories clustering information (c) general marine statistics (cargo types, etc.) and (d) correlation between spatial environmental importance and marine traffic risk. Towards this end, a set of data clustering and probabilistic graphical modelling techniques has been adopted.