A Multi-Annotator Survey of Sub-km Craters on Mars
A Multi-Annotator Survey of Sub-km Craters on Mars
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DOI：
10.3390/data5030070
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发表时间：
2020-09-01
期刊：
影响因子：
2.6
通讯作者：
Muller, Jan-Peter
中科院分区：
文献类型：
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作者：
Francis, Alistair;Brown, Jonathan;Muller, Jan-Peter
我们在这里展示了一个在MC-11东部四边形的火星表面大约1700公里（2）的近5000个小陨石坑的数据集。该数据集涵盖12个2000 × 2000像素的上下文相机图像，每个图像都由6个注释器进行全面标记，其结果使用凝聚聚类进行组合。陨石坑的大小频率分布是至关重要的行星表面年龄的估计，代替原位采样。较老的表面暴露在陨石撞击物下的时间更长，因此坑坑更密集。然而，虽然人们对较大陨石坑的数量有了很好的了解，但对小陨石坑（千米以下）的产生和侵蚀过程的限制却比较少。我们认为，通过调查大量的小陨石坑，行星科学界可以减少一些目前的不确定性，其生产和侵蚀率。为此，许多人试图使用利用深度学习的最先进的对象检测技术，尽管这种技术很强大，但需要大量的标记训练数据才能实现最佳性能。这项调查为研究人员提供了一个大型数据集，用于分析MC-11 East上空的小陨石坑统计数据，并使他们能够更好地训练和验证他们的陨石坑检测算法。这些数据的收集还展示了用于标记许多小对象的多注释器方法，该方法为每个注释和注释器产生估计的置信度得分。
We present here a dataset of nearly 5000 small craters across roughly 1700 km(2)of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results are combined using agglomerative clustering. Crater size-frequency distributions are centrally important to the estimation of planetary surface ages, in lieu of in-situ sampling. Older surfaces are exposed to meteoritic impactors for longer and, thus, are more densely cratered. However, whilst populations of larger craters are well understood, the processes governing the production and erosion of small (sub-km) craters are more poorly constrained. We argue that, by surveying larger numbers of small craters, the planetary science community can reduce some of the current uncertainties regarding their production and erosion rates. To this end, many have sought to use state-of-the-art object detection techniques utilising Deep Learning, which-although powerful-require very large amounts of labelled training data to perform optimally. This survey gives researchers a large dataset to analyse small crater statistics over MC-11 East, and allows them to better train and validate their crater detection algorithms. The collection of these data also demonstrates a multi-annotator method for the labelling of many small objects, which produces an estimated confidence score for each annotation and annotator.