  Horizon or skyline detection plays a vital role towards mountainous visual
geo-localization, however most of the recently proposed visual geo-localization
approaches rely on \textbf{user-in-the-loop} skyline detection methods.
Detecting such a segmenting boundary fully autonomously would definitely be a
step forward for these localization approaches. This paper provides a
quantitative comparison of four such methods for autonomous horizon/sky line
detection on an extensive data set. Specifically, we provide the comparison
between four recently proposed segmentation methods; one explicitly targeting
the problem of horizon detection\cite{Ahmad15}, second focused on visual
geo-localization but relying on accurate detection of skyline \cite{Saurer16}
and other two proposed for general semantic segmentation -- Fully Convolutional
Networks (FCN) \cite{Long15} and SegNet\cite{Badrinarayanan15}. Each of the
first two methods is trained on a common training set \cite{Baatz12} comprised
of about 200 images while models for the third and fourth method are fine tuned
for sky segmentation problem through transfer learning using the same data set.
Each of the method is tested on an extensive test set (about 3K images)
covering various challenging geographical, weather, illumination and seasonal
conditions. We report average accuracy and average absolute pixel error for
each of the presented formulation.
