Due to complicated backgrounds and unclear target orientation, automated object detection is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) object-oriented detection techniques are based on a faster region-based CNN (R-CNN) and other one-stage detectors that often lack adequate processing speeds and detection accuracies. Recently, the two-stage detector Mask R-CNN technique achieved impressive results in object detection and instance segmentation problems and was successfully applied in the analysis of archaeological airborne laser scanning (ALS) data. In this study, we outline a modified Mask R-CNN technique that reliably and efficiently detects relict charcoal hearth (RCH) sites on light detection and ranging (LiDAR) data-based digital elevation models (DEMs).Using image augmentation and image preprocessing steps combined with the deep learning-based adaptive gradient method with a dynamic bound on the learning rate (AdaBound) optimization technique, we could improve the model's accuracy and significantly reduce its training time. We use DEMs based on high-resolution LiDAR data and the visualization for archaeological topography (VAT) technique that give images with a very strong contrast of the terrain and the outline of the sites of interest in the North German Lowland. Therefore, the model can identify RCH sites with an average recall of 83% and an average precision of 87%. Techniques such as the modified Mask R-CNN method outlined here will help to greatly improve our knowledge about archaeological site densities in the realm of historical charcoal production and past human-landscape interactions. This method provides an accurate, time-efficient and bias-free large-scale site mapping option not only for the North German Lowland but potentially for other landscapes as well.

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With the increasing availability of high‐resolution digital elevation models (DEMs), large‐scale mapping of anthropogenic relief features has become feasible for more areas. However, the landscape‐scale distribution patterns of anthropogenic landforms and the quality of DEM‐based mapping can be highly heterogeneous. In this study, we mapped relict charcoal hearths (RCHs) in two study regions with differing environmental backgrounds, covering forest areas of more than 15,000 km2, analyzed the RCH distributions and evaluated possibilities for predictive modeling of RCH occurrence with respect to natural and cultural landscape structures. More than 45,000 RCHs were recorded in each region, with high site densities even in areas remote from charcoal‐consuming industries. Variations in the quality of DEM‐based mapping were related to small‐scale differences in the DEM quality and larger‐scale substrate heterogeneity. A clear association between RCHs and historical industrial sites was found in the Northern European Lowland; while the density of mapped RCHs was predominantly related to geology and morphology in the lower mountain ranges. The results show that variations in mapping quality across scales and the natural and cultural background of a region need to be considered so that the mapping of anthropogenic relief features can contribute to an improved understanding of land‐use history.