Ancient Burial Mounds Detection in the Altai Mountains with High-Resolution Satellite Images

Abstract

Highlights: What are the main findings? Deep learning techniques are applied for automatic kurgan identification. Performance of various CNN-based and Transformer-based object detection methods is comprehensively compared. What is the implication of the main finding? Deep learning techniques are feasible for automatic kurgan identification. Deep learning techniques show strong potential for building a comprehensive inventory of kurgans in Altai Mountains. The Altai Mountains rank among the world’s most notable and valuable archaeological regions. Within the sprawling Altai Mountains area, burial mounds (kurgans) of past civilizations, which are sometimes well preserved in permafrost, are a particularly precious trove of archaeological insights. This study investigates the application of deep learning-based object detection techniques for automatic kurgan identification in high-resolution satellite imagery. We compare the performance of various object detection methods utilizing both convolutional neural network and Transformer backbones. Our results validate the effectiveness of different approaches, especially with larger models, in the challenging task of detecting small archaeological structures. Techniques addressing the class imbalance can further improve performance of off-the-shelf methods. These findings demonstrate the feasibility of employing deep learning techniques to automate kurgan identification, which can improve archaeological surveying processes. It suggests the potential of deep learning technology for constructing a comprehensive inventory of Altai Mountain kurgans, particularly relevant in the context of global warming and archaeological site preservation.