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A vector-based coastline shape classification approach using sequential deep learning model
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DOI：10.1016/j.jag.2024.103810.png)
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Abstract
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Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challenges of accurate description and ambiguous classification rules, automatic classification of vector coastlines has been a difficult but urgent problem to solve. In this paper, we propose a datadriven approach for classifying the shape of vector coastlines, according to their morphological characteristics. The method utilizes a sequence-based deep learning algorithm to model and classify coastline segments. We construct a dataset including five representative types of vector coastlines, train and evaluate the model using this dataset. The evaluation results show that the proposed method outperforms all baselines, achieving a classification accuracy of 93.20%. This method can be integrated into existing Coastal Management Systems to enhance their morphological analysis functions, making a valuable contribution to the applications of Artificial Intelligence (AI) in coastal management.
Keywords:
Coastlines
Classification
Recurrent Neural Networks
Coastal management system
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