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      BoatNet: Automated Small Boat Composition Detection using Deep Learning on Satellite Imagery

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            Abstract

            Tracking and measuring national carbon footprints is one of the keys to achieving the ambitious goals set by countries. 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 has begun to look into the role played by small boat fleets in terms of Greenhouse Gases (GHG), but this either relies on high-level techno-activity assumptions or the installation of GPS 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 GHG emissions. This 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 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 GHG emissions in any given region. The data curated and produced in this study is freely available at https://github.com/theiresearch/BoatNet.

            Content

            Author and article information

            Journal
            UCL Open: Environment Preprint
            UCL Press
            20 July 2022
            Affiliations
            [1 ] UCL Energy Institute, The Bartlett School of Environment, Energy and Resources, University College London, 14 Upper Woburn Place, London WC1H 0NN, UK
            Author notes
            Article
            10.14324/111.444/000177.v1
            d5911bb1-d4d5-492b-8764-d6c3bb87bb38

            This is an open access article distributed under the terms of the Creative Commons Attribution License (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.


            The datasets generated during and/or analysed during the current study are available in the repository: https://github.com/theiresearch/BoatNet
            Earth & Environmental sciences,Computer science,Statistics,Geosciences
            Object Detection,Small boats activity,Energy,Deep Learning,Climate,Climate Change,Transfer Learning,Policy and law,Sustainable development,The Environment,Statistics

            Comments

            Date: 28 July 2022

            Handling Editor: Dr Craig Styan

            This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment for open peer review.

            2022-07-28 13:45 UTC
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