Ongoing efforts to characterize underwater dunes have led to a considerable number of freely available tools that identify these bedforms in a (semi-)automated way. However, these tools differ with regard to their research focus and appear to produce results that are far from unequivocal. We scrutinize this assumption by comparing the results of five recently published dune identification tools in a comprehensive meta-analysis. Specifically, we analyze dune populations identified in three bathymetries under diverse flow conditions and compare the resulting dune characteristics in a quantitative manner. Besides the impact of underlying definitions, it is shown that the main heterogeneity arises from the consideration of a secondary dune scale, which has a significant influence on statistical distributions. Based on the quantitative results, we discuss the individual strengths and limitations of each algorithm, with the aim of outlining adequate fields of application. However, the concerted bedform analysis and subsequent combination of results have another benefit: the creation of a benchmarking data set which is inherently less biased by individual focus and therefore a valuable instrument for future validations. Nevertheless, it is apparent that the available tools are still very specific and that end-users would profit by their merging into a universal and modular toolbox.

中文翻译：

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自动床型识别——对当前方法及其输出异质性的荟萃分析

描述水下沙丘特征的持续努力已经产生了大量免费可用的工具，可以以（半）自动的方式识别这些沙床形态。然而，这些工具的研究重点各不相同，并且产生的结果似乎远非明确。我们通过综合荟萃分析比较最近发布的五种沙丘识别工具的结果来仔细审查这一假设。具体来说，我们分析了在不同水流条件下通过三种测深方法确定的沙丘种群，并以定量方式比较所得的沙丘特征。除了基本定义的影响之外，研究表明主要的异质性是由于考虑了次生沙丘尺度而产生的，这对统计分布有重大影响。根据定量结果，我们讨论每种算法的各自优点和局限性，目的是概述适当的应用领域。然而，一致的床形分析和随后的结果组合还有另一个好处：创建基准数据集，该数据集本质上较少受到个人关注的偏差，因此是未来验证的宝贵工具。然而，很明显，可用的工具仍然非常具体，最终用户将通过将它们合并到通用和模块化工具箱中而受益。