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BoatNet: automated small boat composition detection using deep learning on satellite imagery

Jialeng, Guo; Suárez de la Fuente, Santiago; Smith, Tristan; (2023) BoatNet: automated small boat composition detection using deep learning on satellite imagery. UCL Open Environment , 5 , Article e058. 10.14324/111.444/ucloe.000058. Green open access

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Abstract

Tracking and measuring national carbon footprints is key to achieving the ambitious goals set by the Paris Agreement on carbon emissions. According to statistics, more than 10% of global transportation carbon emissions result from shipping. However, accurate tracking of the emissions of the small boat segment is not well established. Past research looked into the role played by small boat fleets in terms of greenhouse gases, but this has relied either on high-level technological and operational assumptions or the installation of global navigation satellite system sensors to understand how this vessel class behaves. This research is undertaken mainly in relation to fishing and recreational boats. With the advent of open-access satellite imagery and its ever-increasing resolution, it can support innovative methodologies that could eventually lead to the quantification of greenhouse gas emissions. Our work used deep learning algorithms to detect small boats in three cities in the Gulf of California in Mexico. The work produced a methodology named BoatNet that can detect, measure and classify small boats with leisure boats and fishing boats even under low-resolution and blurry satellite images, achieving an accuracy of 93.9% with a precision of 74.0%. Future work should focus on attributing a boat activity to fuel consumption and operational profile to estimate small boat greenhouse gas emissions in any given region.

Type: Article
Title: BoatNet: automated small boat composition detection using deep learning on satellite imagery
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.14324/111.444/ucloe.000058
Publisher version: https://doi.org/10.14324/111.444/ucloe.000058
Language: English
Additional information: Copyright and open access ©2023 The Authors. Creative Commons Attribution Licence (CC BY) 4.0 International licence https://creativecommons.org/licenses/by/4.0/ Open access This is an open access article distributed under the terms of the Creative Commons Attribution Licence (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Keywords: climate change, deep learning, machine learning, object detection, small boat activity, transfer learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10171426
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