GPR data interpretation approaches in archaeological prospectionGPR data interpretation approaches in archaeological prospection Ανασκόπηση Review 2022-10-112021enThe presented results are part of the first author’s Ph.D. dissertation which was partially funded by POLITEIA, Action KRIPIS research project no. MIS-448300 (2013SE01380035), and Stavros Niarchos Foundation—FORTH Fellowships for Ph.D. candidates within the project ARCHERS: Advancing Young Researchers’ Human Capital in Cutting Edge Technologies in the Preservation of Cultural Heritage and the Tackling of Societal Challenges.This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed. http://creativecommons.org/licenses/by/4.0/Applied Sciences1116Manataki_et_al_Appl. Sci._11(16)_2021.pdfChania [Greece]Library of TUC2022-10-11application/pdf2.0 MBfree Manataki Meropi Μανατακη Μεροπη Vafeidis Antonios Βαφειδης Αντωνιος Sarris, Apostolos MDPI Ground Penetrating Radar Archaeological prospection Data interpretation Convolutional Neural Networks AlexNet