{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T17:05:51Z","timestamp":1776186351025,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis\/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.<\/jats:p>","DOI":"10.3390\/rs14143377","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"3377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2937-1855","authenticated-orcid":false,"given":"Melda","family":"K\u00fc\u00e7\u00fckdemirci","sequence":"first","affiliation":[{"name":"Department of Archaeology and Ancient History, Digital ArchaeologyLaboratory (DARKLab), Lund University, 221 00 Lund, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6071-4767","authenticated-orcid":false,"given":"Apostolos","family":"Sarris","sequence":"additional","affiliation":[{"name":"Department of History and Archaeology, Archaeological Research Unit (ARU), Digital Humanities GeoInformatics Laboratory, University of Cyprus, 1678 Nicosia, Cyprus"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18862","DOI":"10.1038\/s41598-020-75994-7","article-title":"Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications","volume":"10","author":"Organista","year":"2020","journal-title":"Sci. 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