In this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen’s Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements.

在本文中，我们比较了两种数据驱动算法在处理地貌学中的自动分类问题时的性能：直接采样 (DS) 和随机森林 (RF)。主要目标是为高山环境的地貌映射提供半自动化程序，使用手动映射的区域作为训练数据集和预测变量来推断目标区域的分类。以前从未研究过 DS 在地貌分类中的适用性。相反，基于 RF 的分类已经在少数研究中应用，但仅限于有限数量的地貌类别。通过将八个检测到的类别与该领域详细说明并被视为基本事实的地貌图进行比较，验证了这两种方法的结果。DS 和 RF 都给出了令人满意的结果，并在准确度和 Cohen 的 Kappa 值方面提供了相似的性能。使用 RF 获得的地图呈现出比使用 DS 时更嘈杂的类空间分布，因为 DS 考虑了不同类的空间依赖性。结果表明，DS 和 RF 都是适用于区域尺度高山环境半自动地貌测绘的技术，为进一步改进开辟了道路。