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\begin{document}

\title{DeepLandforms: A Deep Learning Computer Vision toolset applied to a
prime use case for mapping planetary skylights}



\author[1]{Giacomo Nodjoumi}%
\author[2]{Riccardo Pozzobon}%
\author[3]{Franceso Sauro}%
\author[1]{Angelo Pio Rossi}%
\affil[1]{Jacobs University Bremen}%
\affil[2]{Università degli Studi di Padova}%
\affil[3]{University of Bologna}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\begin{abstract}
Thematic map creation is a process that implies several steps to be
accomplished regardless of the type of the map to be produced, starting
from data collection, through data exploitation and ending with maps
publication as print, image, and GIS format. An example are geological,
lithological, and geomorphological maps, in which most of the highest
time-consuming tasks are those related to the discretization of single
objects by identifying a set of unique characteristics that describe
uniquely those objects. Commonly these tasks introduce interpretative
biases due to the different experience of the mappers who's performing
it. In this setting, Deep Learning Computer Vision techniques could play
a key-role but lack the availability of a complete set of tools specific
for planetary mapping. The aim of this work is to develop a
comprehensive set of ready-to-use tools for landforms mapping, in which
users have full access to the workflow and over all the processes
involved, granting complete control and customization capabilities. In
this work are presented both the developed tools and the approach that
has been used and that is based on consolidated Deep Learning
methodologies and open-source libraries commonly applied in other fields
of Computer Vision. The toolset and the approach presented have been
tested in the science case of mapping sinkhole-like landforms on Mars
and results are presented.%
\end{abstract}%



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