Square ancient sites detection in typical regions of the Mongolian plateau using improved faster R-CNN from Google Earth high-resolution images

Abstract

ABSTRACT Many square ancient sites exist in the Mongolian plateau region, and locating them is of great importance for archaeological research. In recent years, object detection in Google Earth (GE) high-resolution remote sensing (RS) images based on deep learning has driven archaeologists’ research of ancient site detection. Deep learning techniques significantly improve the accuracy of site detection in RS images when compared with traditional detection methods while reducing design and computation time. Based on the characteristics of the research objectives, this paper proposes an improved Faster R-CNN algorithm for the detection of square ancient sites in GE high-resolution RS images, which employs Swin-Transformer, VGG16, Atrous Spatial Pyramid Pooling (ASPP), Squeeze-and-Excitation Networks (SENet) to form an improved backbone network, and three methods to optimize the Region Proposal Network (RPN). In addition, a dataset of known square ancient sites containing four typical regions of the Mongolian plateau is built using GE high-resolution RS images for training and evaluating the algorithm model. The experimental results show that the Precision, Recall, F1, IoU, and Average Precision (AP) of the proposed improved Faster R-CNN are 91.38%, 91.16%, 91.27%, 83.49%, and 93.57%, respectively, which are 34.89%, 2.70%, 22.29%, 31.46%, and 7.32% higher than those of the original Faster R-CNN. The algorithm also has high evaluation metrics for object detection in four typical regions: hills, Gobi, cropland, and grassland. Finally, the proposed algorithm is applied to detect square ancient sites in six selected typical regions, and numerous new ones are discovered. Overall, the proposed algorithm provides an effective method for archaeological investigations in the study region.