Around the world, beach wrack is an essential part of Blue Carbon Ecosystems (BCE); further, it has significant ecological value. The benefit of beach wrack is that it can become a greenhouse gas source as beach wrack decomposes rapidly in coastal environments. On the other hand, a massive accumulation of breach wrack would disturb tourism and the residents. Thus, it is essential to manage the coastal environment effectively by monitoring beach wrack priority. To monitor beach wrack, a manual type may not be cost-effective as labour would be more. The introduction of Unmanned Aerial Vehicles (UAV) also for beach wrack monitoring can be cost-effective and time-saving. UAVs would capture images with spatial resolution. This chapter has investigated the feasibility of beach wrack on different beaches. Object-based image analysis (OIA) was introduced as a method of beach wrack images for classification with different Machine Learning (ML) and Deep Learning (DL) mechanisms. The mechanisms that have been applied to detect beach wracks were K-Nearest Neighbour (KNN), Random Forest (RF), and Back Propagation Workflow based Convolutional Neural Network (BPWCNN) for studying segmentation levels and different feature spaces. The experimental study shows that all ML and DL algorithms have introduced accuracy and precision of more than 90%, and among all algorithms, 94.34% and 95.76%, respectively, as classification accuracy and precision for the BPWCN algorithm. This is concluded that UAV-based beach wrack monitoring would be the best and cost effective mechanism for coastal zone environment monitoring across different parts of the globe.


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    Title :

    Beach Wrack Identification on Unmanned Aerial Vehicles Dataset Using Artificial Intelligence for Coastal Environmental Management


    Additional title:

    Transactions on Computer Systems & Networks


    Contributors:


    Publication date :

    2023-09-27


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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