When rover mission planners are laying out the path for their rover, they use a combination of stereo images and statistical and geological data in order to plot a course for the vehicle to follow for its mission. However, there is a lack of detailed images of the lunar surface that indicate the specific presence of hazards, such as craters, and the creation of such crater maps is time-consuming. There is also little known about how varying lighting conditions caused by the changing solar incidence angle affects perception as well. This paper addresses this issue by investigating how varying the incidence angle of the sun affects how well the human and AI can detect craters. It will also see how AI can accelerate the crater-mapping process, and how well it performs relative to a human annotating crater maps by hand. To accomplish this, several sets of images of the lunar surface were taken with varying incidence angles for the same spot and were annotated both by hand and by an AI. The results are observed, and then the AI performance was rated by calculating its resulting precision and recall, considering the human annotations as being the ground truth. It was found that there seems to be a maximum incidence angle for which detect rates are the highest, and that, at the moment, the AI’s detection of craters is poor, but it can be improved. With this, it can inform future and more expansive investigations into how lighting can affect the perception of hazards to rovers, as well as the role AI can play in creating these crater maps.

Drylands, though one of the largest biomes, are also one of the most understudied biomes on the planet. This leaves scientists with limited understanding of unique life forms that have adapted to live in these arid environments. One such life form is the hypolithic microbial community; these are autotrophic cyanobacteria colonies that can be found on the underside of translucent rocks in deserts. With the light that filters through the rock above them, the microbes can photosynthesize and fix carbon from the atmosphere into the soil. In this study I looked at hypolith-like rock distribution in the Namib Desert by using image recognition software. I trained a Mask R-CNN network to detect quartz rock in images from the Gobabeb site. When the method was analyzed using the entire data set, the distribution of rock sizes between the manual annotations and the network predictions was not similar. When evaluating rock sizes smaller than 0.56 cm2 the method showed statistical significance in support of being a promising data collection method. With more training and corrective effort on the network, this method shows promise to be an accurate and novel way to collect data efficiently in dryland research.