\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Adaptive Super-resolution for Ocean Bathymetric Maps using a Deep Neural
Network and Data Augmentation}



\author[1]{Koshiro Murakami}%
\author[1]{Daisuke Matsuoka}%
\author[2]{Naoki Takatsuki}%
\author[1]{Mitsuko Hidaka}%
\author[1]{Yukari Kido}%
\author[1]{Eiichi Kikawa}%
\affil[1]{Japan Agency for Marine-Earth Science and Technology}%
\affil[2]{IDEA Consultants, Inc.}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Machine learning-based image super-resolution is a robust approach for
obtaining detailed bathymetric maps. However, in machine learning using
supervised data, the dissimilarity in the features of training and
target datasets degrades super-resolution performance. This study
proposes a two-step method to generate training data with features
similar to those of the target data using image transformation and
composition. The super-resolution model trained via the proposed method
on the Central Okinawa Trough data was applied to the bathymetry data
around Okinotorishima Islands. The method improved the root mean squared
error by up to 14.3\% compared to conventional approaches, thus
demonstrating the potential of combining artificial data generation with
machine learning for super-resolution bathymetry mapping of the entire
ocean floor.%
\end{abstract}%



\sloppy


\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig1/fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig2/fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig3/fig3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig4/fig4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig5/fig5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

