No abstract

Analysing the spatial distribution of anthropogenic relief structures can contribute to the understanding of past land‐use systems. With automated mapping routines, small relief forms can be detected efficiently from high‐resolution digital terrain models (DTMs). In this study, we describe an approach for the automated mapping of charcoal kiln sites from an airborne laser‐scanning DTM. The study site is located north of Cottbus, Germany, where an exceptionally large historic charcoal production field has been documented in previous archaeological surveys. The goal of this study was to implement, evaluate and improve an automated GIS‐based routine for mapping these features based on the template‐matching principle. In addition to the DTM, different morphometric variables were evaluated for their suitability to detect kiln sites. The mapping results were validated against a comprehensive database of kiln sites recorded from archaeological excavations and via manual digitization. The effects of irregular kiln‐site geometry and DTM noise were evaluated using synthetic DTMs. The results of the synthetic DTM mapping show that the template‐matching results differed depending on the morphometric variable used for the mapping process. In accordance with this observation, a validation of the mapping procedure for the field site suggests that feature mapping can be improved. In particular, the number of false detections can be reduced using a combination of morphometric variables. For the validation area, the kiln sites with diameters of at least 10 m were mapped using the automated routine, with detection rates that were close to those of manual digitization. Therefore, the described method can considerably facilitate the mapping and distribution analysis of kiln sites or similar small relief forms that are prominent in a specific landscape. Copyright © 2014 John Wiley & Sons, Ltd.

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.

Historic charcoal hearth remains provide a unique archive of the long-term interaction between biochar, soil development, and plant growth. Charcoal as raw material was crucial for production of iron in iron works, and hence numerous charcoal hearths can be found in the forests near historic iron works in Europe and in the eastern united States. Charcoal hearths are round to elliptical forms often around 10 m in diameter and consist of several-decimeter-thick layers that contain charcoal fragments, ash, and burnt soil. We studied the soil chemistry of 24 charcoal hearths and compared them with the surrounding "natural" soils in the northern Appalachians of northwestern Connecticut. The thickness of the topsoils on the charcoal hearths and their carbon content are remarkably higher than in the surrounding topsoils.