Giacomo Nodjoumi Jacobs University Bremen
Detecting Cave Entrance Candidates on Mars using Deep Learning Computer Vision
In the framework of geological exploration of terrestrial planets, sinkhole-like landforms (pit craters, pit chains and skylights), as a potential direct access to the subsurface, are one of the most promising environments to focus our research.
Detecting, mapping, and describing those types of landforms is a challenging process since a set of tedious tasks must be conducted manually by researchers, usually on a small set of available data.
These tasks vary from data collection (in which areas with high probability of occurrences are selected and downloaded) to manual analysis that requires viewing the images in detail, mapping all occurrences with GIS, and extracting morphometric parameters.
For the Moon and Mars, databases of cave candidates exist (see MGC^3 Cushing, 2012) but all of these databases are focused on small regions, rather than at planetary scale.
Thus there are possible missing correlations between the presence of these landforms and the area of detection that can be related to past and present processes.
To achieve data analyses at planetary scale, machine learning and deep learning algorithms are extremely valuables techniques, capable of automatically analysing large datasets.
The main problem is that often it is necessary to develop specific tools and pipelines for this task.
Two jupyter notebooks have been developed around FOSS Object Detection packages and used with a small dataset of 130 high resolution images acquired by the HiRISE camera from the Mars Reconnaissance Orbiter.
The aim of these is to create an user-friendly environment for training and evaluating an object detection model; not only for cave candidates but also for other types of landforms.
These results have been compared to available databases with preliminary promising results.