Detecting Archaeological Phenomena Using Deep Learning in the Study of the Old Aerial Images of Historical City of Zuzan

Abstract

Due to saving time and manpower, automatic and semi-automatic methods can be used to identify and analyse ancient artefacts. Such methods are usually among the studies of neural networks and machine learning systems, which are carried out using remote sensing data and are completely based on spatial information. In the present research, the aim is to detect archaeological phenomena in the landscape of the historical city of Zuzan using convolutional neural network and object detection using the YOLO v8 algorithm, which uses aerial images from the 1960s and 1990s as input data. The most important steps of this method are: training and learning model, image pre-processing, feature extraction and feature labelling are implemented to provide an automatic pattern recognition system for recognizing archaeological phenomena in an urban landscape. The training data set consists of old aerial images in which features such as the city wall (fence), Citadel and Aqueduct (Qanat) are labelled. The results of CNN training with aerial images of the 60s and 90s and Yolo modelling show the detection of feature such as the aqueduct with 69% accuracy, the city wall with 91% accuracy and the citadel with 100% accuracy.