LiDAR data have become indispensable for research in archaeology and a variety of other topographic applications. To derive products (e.g. digital terrain or feature models, individual trees, buildings), the 3D LiDAR points representing the desired objects of interest within the acquired and georeferenced point cloud need to be identified. This process is known as classification, where each individual point is assigned to an object class. In archaeological prospection, classification focuses on identifying the object class ‘ground points’. These are used to interpolate digital terrain models exposing the microtopography of a terrain to be able to identify and map archaeological and palaeoenvironmental features. Setting up such classification workflows can be time-consuming and prone to information loss, especially in geographically heterogeneous landscapes. In such landscapes, one classification setting can lead to qualitatively very different results, depending on varying terrain parameters such as steepness or vegetation density. In this paper, we are focussing on a special workflow for optimal classification results in these heterogeneous environments, which integrates expert knowledge. We present a novel Python-based open-source software solution, which helps to optimize this process and creates a single digital terrain model by an adaptive classification based on spatial segments. The advantage of this approach for archaeology is to produce coherent digital terrain models even in geomorphologically heterogenous areas or areas with patchy vegetation. The software is also useful to study the effects of different algorithm and parameter combinations on digital terrain modelling with a focus on a practical and time-saving implementation. As the developed pipelines and all meta-information are made available with the resulting data set, classification is white boxed and consequently scientifically comprehensible and repeatable. Together with the software's ability to simplify classification workflows significantly, it will be of interest for many applications also beyond the examples shown from archaeology.

中文翻译：

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空间自适应地面点过滤管道的人在回路开发——考古学案例研究

LiDAR 数据已成为考古学研究和各种其他地形学应用不可或缺的一部分。为了获得产品（例如数字地形或特征模型、单独的树木、建筑物），需要识别代表所获取和地理参考点云中所需感兴趣对象的 3D LiDAR 点。此过程称为分类，其中每个单独的点都分配给一个对象类。在考古勘探中，分类侧重于识别对象类“地面点”。这些用于插入显示地形微地形的数字地形模型，以便能够识别和绘制考古和古环境特征。建立这样的分类工作流程可能很耗时，而且容易丢失信息，特别是在地理异质景观中。在这样的景观中，一种分类设置可能会导致质量上非常不同的结果，这取决于不同的地形参数，例如陡度或植被密度。在本文中，我们专注于在这些异构环境中获得最佳分类结果的特殊工作流程，它集成了专家知识。我们提出了一种新颖的基于 Python 的开源软件解决方案，它有助于优化此过程并通过基于空间段的自适应分类创建单一数字地形模型。这种考古方法的优点是即使在地貌异质区域或植被斑驳的区域也能生成连贯的数字地形模型。该软件还可用于研究不同算法和参数组合对数字地形建模的影响，重点是实用且省时的实施。由于开发的管道和所有元信息都可用于生成的数据集，分类是白盒的，因此在科学上是可理解和可重复的。连同该软件显着简化分类工作流程的能力，除了考古学中显示的示例之外，它还会引起许多应用程序的兴趣。