{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:19:29Z","timestamp":1779877169694,"version":"3.53.1"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["101086280"],"award-info":[{"award-number":["101086280"]}]},{"name":"European Union","award":["10100763"],"award-info":[{"award-number":["10100763"]}]},{"name":"EYE","award":["101086280"],"award-info":[{"award-number":["101086280"]}]},{"name":"EYE","award":["10100763"],"award-info":[{"award-number":["10100763"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This comparative study evaluates the performance of three popular deep learning architectures, AlexNet, VGG-16, and VGG-19, on a custom-made dataset of GPR C-scans collected from several archaeological sites. The introduced dataset has 15,000 training images and 3750 test images assigned to three classes: Anomaly, Noise, and Structure. The aim is to assess the performance of the selected architectures applied to the custom dataset and examine the potential gains of using deeper and more complex architectures. Further, this study aims to improve the training dataset using augmentation techniques. For the comparisons, learning curves, confusion matrices, precision, recall, and f1-score metrics are employed. The Grad-CAM technique is also used to gain insights into the models\u2019 learning. The results suggest that using more convolutional layers improves overall performance. Further, augmentation techniques can also be used to increase the dataset volume without causing overfitting. In more detail, the best-obtained model was trained using VGG-19 architecture and the modified dataset, where the training samples were raised to 60,000 images through augmentation techniques. This model reached a classification accuracy of 94.12% on an evaluation set with 170 unseen data.<\/jats:p>","DOI":"10.3390\/rs15123193","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T01:59:30Z","timestamp":1687226370000},"page":"3193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Exploring Deep Learning Models on GPR Data: A Comparative Study of AlexNet and VGG on a Dataset from Archaeological Sites"],"prefix":"10.3390","volume":"15","author":[{"given":"Merope","family":"Manataki","sequence":"first","affiliation":[{"name":"Alma-Sistemi Srl, 00012 Guidonia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-5844","authenticated-orcid":false,"given":"Nikos","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Laboratory of Geophysical Satellite Remote Sensing and Archaeoenvironment, Institute for Mediterranean Studies, Foundation for Research and Technology Hellas, 74100 Rethymno, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-219X","authenticated-orcid":false,"given":"Nikolaos","family":"Schetakis","sequence":"additional","affiliation":[{"name":"Alma-Sistemi Srl, 00012 Guidonia, Italy"},{"name":"Quantum Innovation Pc., 73100 Chania, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Di Iorio","sequence":"additional","affiliation":[{"name":"Alma-Sistemi Srl, 00012 Guidonia, Italy"},{"name":"Quantum Innovation Pc., 73100 Chania, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"ref_1","unstructured":"Conyers, L.B. (2004). Ground-Penetrating Radar for Archaeology, AltaMira Press."},{"key":"ref_2","unstructured":"Goodman, D. (2009). Seeing the Unseen. Geophysics and Landscape Archaeology, Taylor & Francis."},{"key":"ref_3","unstructured":"Manataki, M., Sarris, A., Donati, J.C., Cuenca Garcia, C., and Kalayci, T. (2015). Best Practices of Geoinformatic Technologies for the Mapping of Archaeolandscapes, Archaeopress Archaeology."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Manataki, M., Vafidis, A., and Sarris, A. (2021). GPR Data Interpretation Approaches in Archaeological Prospection. Appl. Sci., 11.","DOI":"10.3390\/app11167531"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fckdemirci, M., and Sarris, A. (2022). GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection. Remote Sens., 14.","DOI":"10.3390\/rs14143377"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep Learning in Medical Image Analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A survey of deep learning techniques for autonomous driving","volume":"37","author":"Grigorescu","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102805","DOI":"10.1016\/j.cviu.2019.102805","article-title":"A survey on deep learning based face recognition","volume":"189","author":"Guo","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56683","DOI":"10.1109\/ACCESS.2021.3069646","article-title":"Plant Disease Detection and Classification by Deep Learning\u2014A Review","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, J., Yang, X., Zhou, F., Li, X., Zhou, B., Lu, S., Ivashov, S., Giannakis, I., Kong, F., and Slob, E. (Comput. Aided Civ. Infrastruct. Eng., 2023). A deep learning framework based on improved self-supervised learning for ground-penetrating radar tunnel lining inspection, Comput. Aided Civ. Infrastruct. Eng., early view.","DOI":"10.1111\/mice.13042"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1111\/mice.12798","article-title":"Deep learning\u2013based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data","volume":"37","author":"Li","year":"2022","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1108\/CI-09-2021-0171","article-title":"Deep learning for detecting distresses in buildings and pavements: A critical gap analysis","volume":"22","author":"Elghaish","year":"2021","journal-title":"Constr. Innov."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/arp.1763","article-title":"Deep learning based automated analysis of archaeo-geophysical images","volume":"27","author":"Sarris","year":"2020","journal-title":"Archaeol. Prospect."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wunderlich, T., Wilken, D., Majchczack, B.S., Segschneider, M., and Rabbel, W. (2022). Hyperbola Detection with RetinaNet and Comparison of Hyperbola Fitting Methods in GPR Data from an Archaeological Site. Remote Sens., 14.","DOI":"10.3390\/rs14153665"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Manataki, M., Vafidis, A., and Sarris, A. (2021, January 1\u20134). Comparing Adam and SGD optimizers to train AlexNet for classifying GPR C-scans featuring ancient structures. Proceedings of the 2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), Valletta, Malta.","DOI":"10.1109\/IWAGPR50767.2021.9843162"},{"key":"ref_16","unstructured":"Abu-Mostafa, Y.S., Magdon-Ismail, M., and Lin, H.-T. (2012). Learning from Data, AMLBook."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press."},{"key":"ref_19","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA. Available online: https:\/\/papers.nips.cc\/paper_files\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html."},{"key":"ref_20","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_21","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_25","unstructured":"Chollet, F., and Keras (2023, May 08). Keras: Deep Learning for Humans. Available online: https:\/\/keras.io\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nmeth.3945","article-title":"Classification evaluation: It is important to understand both what a classification metric expresses and what it hides","volume":"13","author":"Lever","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_28","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1080\/00934690.2017.1365565","article-title":"A regional approach to ancient urban studies in Greece through multi-settlement geophysical survey","volume":"42","author":"Donati","year":"2017","journal-title":"J. Field Archaeol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1080\/00934690.2020.1826749","article-title":"Archaeology and Geophysics in Tandem on Crete","volume":"45","author":"Driessen","year":"2020","journal-title":"J. Field Archaeol."},{"key":"ref_31","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:55Z","timestamp":1760126215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123193"],"URL":"https:\/\/doi.org\/10.3390\/rs15123193","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,20]]}}}