Archeological Sites Classification Through Partial Imaging and Convolutional Neural Networks

Abstract

In this paper, a novel approach for classifying archeological sites using publicly available images through the use of Convolutional Neural Networks (CNNs) is presented. To surmount the problem of having a limited amount of data to use in training and testing the CNNs, our approach employs the technique of fine tuning. We conducted an experiment with four popular CNN architectures: VGG-16, VGG-19, ResNet50, and InceptionV3. The results show that our models achieved an impressive accuracy of up to 98% using the VGG-16 and InceptionV3 models and up to 97% using the ResNet50 model, while the VGG-19 model produced results with an accuracy of 95%. The results of this study demonstrate the effectiveness of our proposed approach in classifying archeological sites using publicly available images and highlight the potential of deep learning techniques for archeological site classification.