Abstract Planetary exploration is full of challenges. Data bandwidth is very limited between planetary rovers and ground-based data system. What’s worse, even though NASA has accumulated over 34 million images from various missions, it requires significant effort and is hardly possible for any scientist to go through all of them. In order to improve the degree of automation and the efficiency of these processes, we propose a system leveraging machine learning for planetary rovers to actively look for scientifically interesting and valuable features according to text instructions from scientists and prioritize the images captured onboard with those features for downlink. Such an image prioritization mechanism can also be naturally applied to content-based image search through text description in any local planetary image data server, allowing scientists to search for images with desired features without going through them one by one. Besides theoretical and engineering details of our proposed approach, we also present both quantitative and qualitative evaluation of the system along with some concrete examples.

中文翻译：

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SCOTI：用于数据优先级排序和本地图像搜索的地形图像科学字幕

摘要 行星探索充满挑战。行星探测器和地面数据系统之间的数据带宽非常有限。更糟糕的是，尽管 NASA 已经从各种任务中积累了超过 3400 万张图像，但它需要付出巨大的努力，而且任何科学家都几乎不可能将它们全部看完。为了提高这些过程的自动化程度和效率，我们提出了一个系统，利用行星漫游者的机器学习，根据科学家的文本指令积极寻找科学上有趣和有价值的特征，并优先考虑具有这些特征的机载图像。下行链路。这种图像优先级机制也可以自然地应用于任何本地行星图像数据服务器中通过文本描述的基于内容的图像搜索，允许科学家搜索具有所需特征的图像，而无需一一浏览。除了我们提出的方法的理论和工程细节外，我们还提供了系统的定量和定性评估以及一些具体示例。