Mapping planetary surfaces is an intricate task that forms the basis for many geologic, geomorphologic, and geographic studies of planetary bodies. In this work, we present a method to automate a specific type of planetary mapping, geomorphic mapping, taking machine learning as a basis. Additionally, we introduce a novel dataset, termed DoMars16k, which contains 16,150 samples of fifteen different landforms commonly found on the Martian surface. We use a convolutional neural network to establish a relation between Mars Reconnaissance Orbiter Context Camera images and the landforms of the dataset. Afterwards, we employ a sliding-window approach in conjunction with a Markov Random field smoothing to create maps in a weakly supervised fashion. Finally, we provide encouraging results and carry out automated geomorphological analyses of Jezero crater, the Mars2020 landing site, and Oxia Planum, the prospective ExoMars landing site.

中文翻译：

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DoMars16k：用于火星上弱监督地貌分析的不同数据集

绘制行星表面图是一项复杂的任务，它构成了许多有关行星体的地质，地貌和地理研究的基础。在这项工作中，我们提出了一种以机器学习为基础的自动化特定类型的行星映射，地貌映射的方法。此外，我们引入了一个称为DoMars16k的新颖数据集，其中包含火星表面上常见的15种不同地貌的16,150个样本。我们使用卷积神经网络在“火星侦察轨道卫星上下文相机”图像和数据集的地形之间建立关系。之后，我们将滑动窗口方法与马尔可夫随机场平滑技术结合起来，以弱监督的方式创建地图。最后，我们提供令人鼓舞的结果，并对Jezero陨石坑进行自动地貌分析，