  Before the seasonal polar ice cap starts to expand towards lower latitudes on
Mars, small frost patches may condensate out during the cold night and they may
remain on the surface even during the day in shady areas. If ice in these areas
can persist before the arrival of the contiguous ice cap, they may remain after
the recession of it too, until the irradiation increases and the ice is met
with direct sunlight. In case these small patches form periodically at the same
location, slow chemical changes might occur as well. To see the spatial and
temporal occurrence of such ice patches, large number of optical images should
be searched for and checked. The aim of this study is to survey the ice
condensation period on the surface with an automatized method using a
Convolutional Neural Network (CNN) applied to High-Resolution Imaging Science
Experiment (HiRISE) imagery from the Mars Reconnaissance Orbiter mission. The
CNN trained to recognise small ice patches is automatizing the search, making
it feasible to analyse large datasets. Previously a manual image analysis was
conducted on 110 images from the southern hemisphere, captured by the HiRISE
camera. Out of these, 37 images were identified with smaller ice patches, which
were used to train the CNN. This approach is applied now to find further images
with potential water ice patches in the latitude band between -40{\deg} and
-60{\deg}, but contrarily to the training dataset recorded between
140-200{\deg} solar longitude, the images were taken from the condensation
period between Ls = 0{\deg} to 90{\deg}. The model was ran on 171 new HiRISE
images randomly picked from the given period between -40{\deg} and -60{\deg}
latitude band, creating 73155 small image chunks. The model classified 2 images
that show small, probably recently condensed frost patches and 327 chunks were
predicted to show ice with more than 60% probability.
