10:45 AM - 11:00 AM
[PPS06-11] The development of craters distribution analytisys tool for Enceladus using a machine laerning and its topographic understanding
Keywords:machine learning, Enceladus
In 2007, the Cassini spacecraft discovered that Enceladus, a small Saturn ice satellite about 500 km in diameter, has a large store of liquid water (internal ocean). Since then, there have been many discussions on the future exploration of Enceladus, which has been suggested as a possible site for life.
The purpose of this study is to develop a machine-learning crater counting tool for Enceladus, and to use the results of this tool to examine its topography in order to deepen our understanding of the surface topography of Enceladus.
In this study, we used YOLOv8 and were able to create a crater detection model with sufficient accuracy for crater mapping. The crater maps were comparable to those in the previous literature, suggesting the influence of Enceladus orbiting Saturn in the same plane due to tidal locking and the influence of surface ice metamorphism, as pointed out in the same literature. In addition, we found that the resolution of the source image has a significant impact on object detection.
The purpose of this study is to develop a machine-learning crater counting tool for Enceladus, and to use the results of this tool to examine its topography in order to deepen our understanding of the surface topography of Enceladus.
In this study, we used YOLOv8 and were able to create a crater detection model with sufficient accuracy for crater mapping. The crater maps were comparable to those in the previous literature, suggesting the influence of Enceladus orbiting Saturn in the same plane due to tidal locking and the influence of surface ice metamorphism, as pointed out in the same literature. In addition, we found that the resolution of the source image has a significant impact on object detection.