*2.2. The Synthetic Dataset*

The proposed synthetic algorithm aims to generate a large amount of images and the corresponding pixel-level annotations with limited manual annotations. Although planetary exploration provides numerous visual data, they have barely been pixel-level annotated. Labor-saving annotation is a vital and usual challenge for planetary visual data. The target of the synthetic algorithm is to build a labor-saving solution to generate a large amount of images and corresponding pixel-level annotations for the pre-training process. Planetary explorations are expensive regarding labor, time, and resource, while the synthetic approach aims to minimize the associated costs. Although multi-labeler seems a promising solution for suppressing human errors, it will further increase the labor and time required. The proposed synthetic algorithm can generate pixel-level annotations while generating synthesized images. To maintain the labor-saving and annotation quality, the following four highlights are essential for designing the synthetic algorithm.

