A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland

• Published in: Archaeological prospection 2021-04, Vol.28 (2), p.177-186
• Main Authors: Bonhage, Alexander, Eltaher, Mahmoud, Raab, Thomas, Breuß, Michael, Raab, Alexandra, Schneider, Anna
• Format: Article
• Language: English
• Subjects: anthropogenic geomorphology; Archaeological sites; Archaeology; automated detection; Automation; Charcoal; charcoal hearth; deep learning; Elevation; Historic buildings & sites; Lidar; Mask R‐CNN; Neural networks; object detection; Optimization; Optimization techniques
• ISSN: 1075-2196; 1099-0763
• DOI: 10.1002/arp.1806

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.