10
views
0
recommends
+1 Recommend
0
shares
    • Review: found
    Is Open Access

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

    EDITOR
    Bookmark
    4
    BoatNet: Automated Small Boat Composition Detection using Deep Learning on Satellite ImageryCrossref
    Average rating:
        Rated 4 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 4 of 5.
    Level of completeness:
        Rated 4 of 5.
    Level of comprehensibility:
        Rated 4 of 5.
    Competing interests:
    None

    Reviewed article

    • Record: found
    • Abstract: found
    • Article: found
    Is Open Access

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

    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.
      Bookmark

      Review information

      10.14293/S2199-1006.1.SOR-EARTH.AQKMYC.v1.RATMTQ
      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Earth & Environmental sciences,Computer science,Statistics,Geosciences
      Small boats activity,Object Detection,Statistics,Climate Change,Energy,Deep Learning,Transfer Learning,Climate,Policy and law,The Environment,Sustainable development
      ScienceOpen disciplines:
      Keywords:

      Review text

      The author of this manuscript reveals that in the design of the energy emission model it is also necessary to consider small boat fleets, in this case in the form of fishing and recreational boats.


      In this paper, the author shows that BoatNet small boat detection model, can detect, measure, and classify small boats
      even under low-resolution and blurry satellite images.

      However, there are some shortcomings in this paper. Authors should explore YOLOv5l which was chosen as the model to train the dataset. Authors should present the YOLOv5l architecture and cite previous research using YOLO5vl.
      In addition, it is necessary to include GPU resources and the employed framework along with computational cost analysis.
      To enrich the analysis, the author should add a comparison of research results with other methods.

      Comments

      Comment on this review