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

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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.

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Author and article information

Journal

Journal ID (nlm-ta): UCL Open Environ

Journal ID (publisher-id): UCLOE

Title: UCL Open Environment

Abbreviated Title: UCL Open Environ

Publisher: UCL Press (UK )

ISSN (Electronic): 2632-0886

Publication date (Electronic, pub): 24 May 2023

Publication date (Electronic, collection): 2023

Volume: 5

Electronic Location Identifier: e058

Affiliations

[1 ]UCL Energy Institute, The Bartlett School of Environment, Energy and Resources, University College London, London, UK

Author notes

*Corresponding author: E-mail: jialeng.guo@ 123456ucl.ac.uk

Author information

Guo Jialeng https://orcid.org/0000-0003-1640-5443

Santiago Suárez de la Fuente https://orcid.org/0000-0002-8787-8531

Tristan Smith https://orcid.org/0000-0002-1925-169X

Article

DOI: 10.14324/111.444/ucloe.000058

PMC ID: 10208328

SO-VID: c0ed039a-8cd9-4da1-9827-1036646272f2

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Licence (CC BY) 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.