The proposed tool will be based on mature technologies in artificial intelligence, machine learning, and pattern recognition and will be trainable by scientists for particular situations or approaches to analysis. Training examples will be provided by the user in the same or higher quality datasets by locating and labeling candidate craters. Learning algorithms can then use the training data to produce a classification scheme that will detect and identify other targets in the images to be studied. The nature of the problem is such that the task lends itself to being generalized, obviating the need to continually develop new tools for different data sets. In addition to automating a laborious and visually-intensive task, the proposed tool will provide the means for an objective, examinable, and repeatable process for detecting and classifying craters.
Craters are among the chief geological features of interest to planetary geologists.
They can be found on all solid-surface solar system bodies. In the absence of direct sample returns from the surface of a planet, satellite, or asteroid, craters are the primary means of establishing the age of the surface or surface units. By establishing a ground-truth using, for example, rock samples dated directly from radionuclides, we can sometimes establish absolute ages of surfaces through the cratering record. Craters, more than any other geological feature, can reveal the history of a surface. The need is not only to keep up with the data volume, but also to allow maximal extraction of useful information from the data, acquired at high cost to the public and to competing science programs.