Planetary science research involves analysing vast amounts of remote sensing data, which are often costly and time-consuming to annotate and process. One of the essential tasks in this field is geological mapping, which requires identifying and outlining regions of interest in planetary images, including geological features and landforms. However, manually labelling these images is a complex and challenging task that requires significant domain expertise and effort. To expedite this endeavour, we propose the use of knowledge distillation using the recently introduced cutting-edge Segment Anything (SAM) model. We demonstrate the effectiveness of this prompt-based foundation model for rapid annotation and quick adaptability to a prime use case of mapping planetary skylights. Our work reveals that with a small set of annotations obtained with the right prompts from the model and subsequently training a specialised domain decoder, we can achieve satisfactory semantic segmentation on this task. Key results indicate that the use of knowledge distillation can significantly reduce the effort required by domain experts for manual annotation and improve the efficiency of image segmentation tasks. This approach has the potential to accelerate extra-terrestrial discovery by automatically detecting and segmenting Martian landforms.

翻译：行星科学研究涉及对海量遥感数据的分析，而标注和处理这些数据往往成本高昂且耗时费力。地质制图是该领域的关键任务之一，需要识别并勾勒出行星图像中的感兴趣区域，包括地质特征与地貌。然而，人工标注这些图像是一项复杂且极具挑战性的工作，需要大量领域专业知识和投入。为加速这一进程，我们提出利用最新引入的尖端模型Segment Anything (SAM)进行知识蒸馏。我们展示了这种基于提示的基座模型在快速标注以及快速适配行星天窗制图这一典型应用场景中的有效性。研究表明，通过从该模型获取正确提示后获得少量标注样本，并据此训练专用领域解码器，即可在该任务上实现令人满意的语义分割效果。关键结果表明，采用知识蒸馏方法可显著减少领域专家进行人工标注的工作量，并提升图像分割任务的效率。该方法通过自动检测与分割火星地貌，有望加速地外探索进程。