Mira Gergácz, Ákos Keresztúri (2023.06.01 - 08.31)

Publication: Analysing high resolution digital Mars images using machine learning

Abstract: The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the HiRISE camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading.

In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40° and -60°. Previously analysed HiRISE images are used to train the model, expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise predictions.

Using a CNN model may make it realistic to analyse all available surface images, aiding us in selecting areas for further investigation.