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jsta committed Mar 25, 2024
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\vspace{-2em}
\section*{Keypoints}
\begin{enumerate}
\item We demonstrate a deep learning pipeline for waterbody database creation with a novel waterbody classifier approaching 80\% accuracy
\item It produced similar results in terms of waterbody count, surface area, and types as a much more expensive to produce hand-curated product
\item We demonstrate a deep learning pipeline for waterbody database creation which includes a novel classifier having nearly 80\% accuracy
\item The pipeline produces similar results for waterbody count, surface area, and type as expensive to produce hand-curated products
\item The modular design of the pipeline is future-proof to forthcoming data products and is easily tailored to specific hydrologic routing tasks
\end{enumerate}
\newpage
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\section{Data Availability Statement}

All data and code associated with this manuscript will be made available at: \href{https://doi.org}{https://doi.org}
All data and code associated with this manuscript will be made available by the time of publication (or earlier pending the conclusion of US Department of Energy approvals) with a Zenodo DOI at: \href{https://doi.org}{https://doi.org}

\section{Acknowledgements}

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