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\begin{document}

\title{Quadtree Decomposition-based Deep Learning Method for Multiscale
Coastline Extraction with High-Resolution Remote Sensing Imagery}



\author[1]{Shuting Sun}%
\author[2]{Lin Mu}%
\author[1]{Ruyi Feng}%
\author[3]{Yifu Chen}%
\author[1]{Lizhe Wang}%
\affil[1]{China University of Geosciences}%
\affil[2]{College of Oceanography, China University of Geosciences}%
\affil[3]{China University of Geosciences (Wuhan)}%


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  \date{June 13, 2024}


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\begin{abstract}
The coastal zone is one of the most important features on the earth's
surface; therefore, it is imperative to extract the coastline, a
representative coastal zone feature with high quality. Previously,
related methods mainly focus on edge and small-scale information, when
processing large scale images, misclassification can occur because it's
difficult to determine whether a local area belongs to land or sea. To
solve this problem, in this study, a deep learning-based multiscale
coastal line extraction algorithm is proposed, whose core is a scene
classification-based multiscale coastal zone classifier to identify the
coastal zones from low to high levels using quadtree decomposition.
Compared to the conventional method, the proposed method can obtain
information from a large receptive field so as to identify land and sea
precisely in high resolution imagery. The results indicate that the
proposed method can effectively eliminate confusing features, and is of
high calculation speed.%
\end{abstract}%



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