Three Percent is Enough: Semi-Supervised Martian Segmentation Labeling With Active Learning

Abstract

Accurate, large-scale Martian segmentation datasets are a cornerstone of autonomous scene understanding in support of exploration and navigation in Martian environments. However, high-quality segmentation labeling on planetary images requires annotators to have professional extraterrestrial geological knowledge, and even skilled annotators need a long time to label a single image in detail. In this paper, we propose a semi-automatic annotation method for Martian scene segmentation. By integrating a semi-supervised segmentation network architecture with an active learning strategy, our framework achieves near-fully supervised performance using a minimal amount of manual annotations, significantly reducing dependence on human experts. Experiments on the S$^{5}$Mars dataset show that our framework reaches 77.01% mIoU with only 3.13% of the manual annotations (169 images), corresponding to 92.23% of the performance (83.50% mIoU) of the official fully supervised model trained on 5400 labeled images. We also conducted an extended experiment on AI4Mars, where the proposed framework consistently achieved strong performance, exceeding the fully supervised baseline by 8.12% using only 3.12% of labeled data. This annotation ratio is substantially lower than the nearly 20% labeled data typically required by conventional semi-supervised approaches, highlighting the efficiency of our method for large-scale Martian scene annotation.