Landslide research relies on landslide inventories for a multitude of spatial, temporal, or process analyses. Generally, it takes high effort to populate a landslide inventory with relevant data. In this context, the present work investigated an effective way to handle vast amounts of automatically acquired digital data for landslide inventories by the use of machine learning algorithms and information filtering. Between July 2017 and February 2019, a keyword alert system provided 4381 documents that were automatically processed to detect landslide events in Germany. Of all those documents, 91% were automatically recognized as irrelevant or duplicates; thereby, the data volume was significantly reduced to contain only actual landslide documents. Moreover, it was shown that inclusion of the document’s images into the automated process chain for information filtering is recommended, since otherwise unobtainable important information was found in them. Compared with manual methods, the automated process chain eliminated personal idiosyncrasies and human error and replaced it with a quantifiable machine error. The applied individual algorithms for natural language processing, information retrieval, and classification have been tried and tested in their respective fields. Furthermore, the proposed method is not restricted to a specific language or region. All languages on which these algorithms are applicable can be used with the proposed method and the training of the process chain can take any geographical restriction into account. Thus, the present work introduced a method with a quantifiable error to automatically classify and filter large amounts of data during automated digital data acquisition for landslide inventories.

中文翻译：

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滑坡清单的自动数字数据采集

滑坡研究依赖滑坡清单进行大量空间、时间或过程分析。通常，用相关数据填充滑坡清单需要付出很大的努力。在这种情况下，目前的工作研究了一种有效的方法，通过使用机器学习算法和信息过滤来处理大量自动获取的滑坡清单数字数据。2017 年 7 月至 2019 年 2 月期间，关键字警报系统提供了 4381 份文件，这些文件被自动处理以检测德国的滑坡事件。在所有这些文件中，91% 被自动识别为不相关或重复；因此，数据量显着减少，只包含实际的滑坡文件。而且，结果表明，建议将文档图像包含在信息过滤的自动化处理链中，因为在其中发现了否则无法获得的重要信息。与手动方法相比，自动化流程链消除了个人特质和人为错误，取而代之的是可量化的机器错误。应用于自然语言处理、信息检索和分类的个别算法已经在各自领域进行了尝试和测试。此外，所提出的方法不限于特定的语言或地区。这些算法适用的所有语言都可以与所提出的方法一起使用，并且流程链的训练可以考虑任何地理限制。因此，