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Fusion of Multi-modal Underwater Ship Inspection Data with Knowledge Graphs

Abstract

The increase in exploitation of marine resources draws significant attention to the
importance of underwater ship inspections which is currently performed based
on regular visual underwater observations carried out by remotely operated vehicles
(ROVs) through collection of various data. In this work we propose to
perform multi-modal data fusion where Knowledge Graphs are chosen for data
representation for automated report generation by enabling big data analytics.

Category

Poster

Client

  • Research Council of Norway (RCN) / 317854

Language

English

Author(s)

  • Joseph Hirsch
  • Brian Elvesæter
  • Alexandre Cardaillac
  • Bernhard Bauer
  • Maryna Waszak

Affiliation

  • University of Augsburg
  • SINTEF Digital / Sustainable Communication Technologies
  • Norwegian University of Science and Technology

Presented at

NorwAI innovate

Place

Trondheim, Norway

Date

01.11.2022 - 02.11.2022

Organizer

NTNU

Year

2022

View this publication at Cristin