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\begin{document}

\title{DeepBedMap: Resolving the bed topography of Antarctica with a deep
neural network}



\author[1]{Wei Ji Leong}%
\author[1]{Huw Horgan}%
\affil[1]{Antarctic Research Centre, Victoria University of Wellington, New Zealand}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
To better resolve the bed elevation of Antarctica, we present DeepBedMap
- a deep learning method that produces realistic Antarctic bed
topography from multiple remote sensing data inputs. Our
super-resolution deep convolutional neural network model is trained on
scattered regions in Antarctica where high resolution (250 m)
groundtruth bed elevation grids are available, and then used to generate
high resolution bed topography in less well surveyed areas. DeepBedMap
takes in a low resolution (1000 m) BEDMAP2 dataset alongside other high
spatial resolution inputs such as ice surface elevation, velocity and
snow accumulation to generate a four times upsampled (250 m) bed
topography map even in the absence of ice-thickness data from direct
seismic or ice-penetrating radar surveys. Our DeepBedMap model is based
on an Enhanced Super Resolution Generative Adversarial Network
architecture that is adapted to minimize per-pixel elevation errors
while producing realistic topography. We show that DeepBedMap offers a
more realistic topographic roughness profile compared to a standard
bicubic interpolated BEDMAP2, and also run model inversions to compare
the basal traction of our DeepBedMap\_DEM with other bed elevation
models.%
\end{abstract}%



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