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Machine Learning Applications in Archaeological Practices: A Review Cover

Machine Learning Applications in Archaeological Practices: A Review

Journal of Computer Applications in Archaeology
Volume 8 (2025): Issue 1
By: Mathias Bellat,  Jordy Didier Orellana Figueroa,  Jonathan Scott Reeves,  Ruhollah Taghizadeh-Mehrjardi,  Claudio Tennie and  Thomas Scholten  
Open Access
|Dec 2025

References

  1. 1Abellán, N, Baquedano, E and Domínguez-Rodrigo, M. 2022. ‘High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms’. Geobios, 72–73: 12–21. DOI: 10.1016/j.geobios.2022.07.001
    Back to article
  2. 2Abitbol, R, Shimshoni, I and Ben-Dov, J. 2021. ‘Machine Learning Based Assembly of Fragments of Ancient Papyrus’. ACM Journal on Computing and Cultural Heritage, 14(3): 33. DOI: 10.1145/3460961
    Back to article
  3. 3Adams, EW and Adams, WY. (eds.) 1991. ‘The Typological Debate’. In: Archaeological Typology and Practical Reality: A Dialectical Approach to Artifact Classification and Sorting. Cambridge: Cambridge University Press. pp. 265–277. DOI: 10.1017/CBO9780511558207.029
    Back to article
  4. 4Agapiou, A and Lysandrou, V. 2015. ‘Remote sensing archaeology: Tracking and mapping evolution in European scientific literature from 1999 to 2015’. Journal of Archaeological Science: Reports, 4: 192–200. DOI: 10.1016/j.jasrep.2015.09.010
    Back to article
  5. 5Agapiou, A and Lysandrou, V. 2023. ‘Interacting with the Artificial Intelligence (AI) Language Model ChatGPT: A Synopsis of Earth Observation and Remote Sensing in Archaeology’. Heritage, 6(5): 4072–4085. DOI: 10.3390/heritage6050214
    Back to article
  6. 6Agapiou, A, Vionis, A and Papantoniou, G. 2021. ‘Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries’. Land, 10(12). DOI: 10.3390/land10121365
    Back to article
  7. 7Albertini, N, Brogni, A, Olivito, R, Taccola, E, Caramiaux, B and Gillies, M. 2017. ‘Designing natural gesture interaction for archaeological data in immersive environments’. Virtual Archaeology Review, 8(16): 12–21. DOI: 10.4995/var.2016.5872
    Back to article
  8. 8Aldenderfer, M. 1998. ‘Quantitative Methods in Archaeology: A Review of Recent Trends and Developments’. Journal of Archaeological Research, 6(2): 91–120. DOI: 10.1023/A:1022893621306
    Back to article
  9. 9Allik, J, Lauk, K and Realo, A. 2020. ‘Factors Predicting the Scientific Wealth of Nations’. Cross-Cultural Research, 54(4): 364–397. DOI: 10.1177/1069397120910982
    Back to article
  10. 10Alpaydin, E. 2014. Introduction to machine learning. Adaptive computation and machine learning. Third edition. Cambridge, Massachusetts: The MIT Press.
    Back to article
  11. 11Altaweel, M, Khelifi, A, Li, Z, Squitieri, A, Basmaji, T and Ghazal, M. 2022. ‘Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results’. Remote Sensing, 14(3). DOI: 10.3390/rs14030553
    Back to article
  12. 12Altaweel, M and Squitieri, A. 2019. ‘Finding a Relatively Flat Archaeological Site with Minimal Ceramics: A Case Study from Iraqi Kurdistan’. Journal of Field Archaeology, 44(8): 523–537. DOI: 10.1080/00934690.2019.1662269
    Back to article
  13. 13Andersen, JP, Degn, L, Fishberg, R, Graversen, EK, Horbach, SPJM, Schmidt, EK, Schneider, JW and Sørensen, MP. 2025. ‘Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions’. Technology in Society, 81: 102813. DOI: 10.1016/j.techsoc.2025.102813
    Back to article
  14. 14Anglisano, A, Casas, L, Queralt, I and Di Febo, R. 2022. ‘Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments’. Sustainability, 14(18): 11214. DOI: 10.3390/su141811214
    Back to article
  15. 15Anichini, F, Dershowitz, N, Dubbini, N, Gattiglia, G, Itkin, B and Wolf, L. 2021. ‘The automatic recognition of ceramics from only one photo: The ArchAIDE app’. Journal of Archaeological Science-Reports, 36. DOI: 10.1016/j.jasrep.2020.102788
    Back to article
  16. 16Anichini, F and Gattiglia, G. 2022. ‘Reflecting on artificial intelligence and archaeology: the ArchAIDE perspective’. Post – Classical Archaeologies, 12: 69–86.
    Back to article
  17. 17Aramendi, J, Arriaza, M, Yravedra, J, Mate-Gonzalez, M, Ortega, M, Courtenay, L, Gonzalez-Aguilera, D, Gidna, A, Mabulla, A, Baquedano, E and Dominguez-Rodrigo, M. 2019. ‘Who ate OH80 (Olduvai Gorge, Tanzania)? A geometric-morphometric analysis of surface bone modifications of a Paranthropus boisei skeleton’. Quaternary International, 517: 118–130. DOI: 10.1016/j.quaint.2019.05.029
    Back to article
  18. 18Argyrou, A and Agapiou, A. 2022. ‘A Review of Artificial Intelligence and Remote Sensing for Archaeological Research’. Remote Sensing, 14(23). DOI: 10.3390/rs14236000
    Back to article
  19. 19Arponen, VPJ, Dörfler, W, Feeser, I, Grimm, S, Groß, D, Hinz, M, Knitter, D, Müller-Scheeßel, N, Ott, K and Ribeiro, A. 2019. ‘Environmental determinism and archaeology’. Understanding and evaluating determinism in research design. Archaeological Dialogues, 26(01): 1–9. DOI: 10.1017/S1380203819000059
    Back to article
  20. 20Bachute, MR and Subhedar, JM. 2021. ‘Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms’. Machine Learning with Applications, 6: 100164. DOI: 10.1016/j.mlwa.2021.100164
    Back to article
  21. 21Badawy, WM, Dmitriev, AYu, Koval, VYu, Smirnova, VS, Chepurchenko, OE, Lobachev, VV, Belova, MO and Galushko, AM. 2022. ‘Formation of reference groups for archaeological pottery using neutron activation and multivariate statistical analyses’. Archaeometry, 64(6): 1377–1393. DOI: 10.1111/arcm.12793
    Back to article
  22. 22Banasiak, P, Berezowski, P, Zaplata, R, Mielcarek, M, Duraj, K and Sterenczak, K. 2022. ‘Semantic Segmentation (U-Net) of Archaeological Features in Airborne Laser Scanning-Example of the Bialowieza Forest’. Remote Sensing, 14(4). DOI: 10.3390/rs14040995
    Back to article
  23. 23Barberena, R, Cardillo, M, Lucero, G, le Roux, PJ, Tessone, A, Llano, C, Gasco, A, Marsh, EJ, Nuevo-Delaunay, A, Novellino, P, Frigolé, C, Winocur, D, Benítez, A, Cornejo, L, Falabella, F, Sanhueza, L, Santana Sagredo, F, Troncoso, A, Cortegoso, V, Durán, VA and Méndez, C. 2021. ‘Bioavailable Strontium, Human Paleogeography, and Migrations in the Southern Andes: A Machine Learning and GIS Approach’. Frontiers in Ecology and Evolution, 9: 584325. DOI: 10.3389/fevo.2021.584325
    Back to article
  24. 24Barragán-Montero, A, Javaid, U, Valdés, G, Nguyen, D, Desbordes, P, Macq, B, Willems, S, Vandewinckele, L, Holmström, M, Löfman, F, Michiels, S, Souris, K, Sterpin, E and Lee, JA. 2021. ‘Artificial intelligence and machine learning for medical imaging: A technology review’. Physica Medica, 83: 242–256. DOI: 10.1016/j.ejmp.2021.04.016
    Back to article
  25. 25Barredo Arrieta, A, Díaz-Rodríguez, N, Del Ser, J, Bennetot, A, Tabik, S, Barbado, A, Garcia, S, Gil-Lopez, S, Molina, D, Benjamins, R, Chatila, R and Herrera, F. 2020. ‘Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI’. Information Fusion, 58: 82–115. DOI: 10.1016/j.inffus.2019.12.012
    Back to article
  26. 26Bataille, CP, Crowley, BE, Wooller, MJ and Bowen, GJ. 2020 Advances in global bioavailable strontium isoscapes. Palaeogeography, Palaeoclimatology, Palaeoecology, 555: 109849. DOI: 10.1016/j.palaeo.2020.109849
    Back to article
  27. 27Bataille, CP, Jaouen, K, Milano, S, Trost, M, Steinbrenner, S, Crubezy, E and Colleter, R. 2021. ‘Triple sulfur-oxygen-strontium isotopes probabilistic geographic assignment of archaeological remains using a novel sulfur isoscape of western Europe’. PLOS One, 16(5): e0250383. DOI: 10.1371/journal.pone.0250383
    Back to article
  28. 28Bataille, CP, von Holstein, ICC, Laffoon, JE, Willmes, M, Liu, X-M and Davies, GR. 2018. ‘A bioavailable strontium isoscape for Western Europe: A machine learning approach’. PLOS One, 13(5): e0197386. DOI: 10.1371/journal.pone.0197386
    Back to article
  29. 29Batist, Z and Roe, J. 2024. ‘Open Archaeology, Open Source? Collaborative practices in an emerging community of archaeological software engineers’. Internet Archaeology, (67). DOI: 10.11141/ia.67.13
    Back to article
  30. 30Bellat, M and Scholten, T. 2024. Automated features detection in archaeology: Standardisation in the area of big data.
    Back to article
  31. 31Bellat, M, Tagizadeh-Mehrjardi, R and Scholten, T. 2024. Fail and try again: Return on topic modelling apply to archaeological scientific literature.
    Back to article
  32. 32Benner, J, Knudby, A, Nielsen, J, Krawchuk, M and Lertzman, K. 2019. ‘Combining data from field surveys and archaeological records to predict the distribution of culturally important trees’. Diversity and Distributions, 25(9): 1375–1387. DOI: 10.1111/ddi.12947
    Back to article
  33. 33Berganzo-Besga, I, Orengo, HA, Lumbreras, F, Carrero-Pazos, M, Fonte, J and Vilas-Estevez, B. 2021. ‘Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia’. Remote Sensing, 13(20): 4181. DOI: 10.3390/rs13204181
    Back to article
  34. 34Berganzo-Besga, I, Orengo, H, Lumbreras, F, Aliende, P and Ramsey, M. 2022. ‘Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms’. Journal of Archaeological Science, 148. DOI: 10.1016/j.jas.2022.105654
    Back to article
  35. 35Bevan, A. 2015. ‘The data deluge’. Antiquity, 89(348): 1473–1484. DOI: 10.15184/aqy.2015.102
    Back to article
  36. 36Bickler, SH. 2018. ‘Machine Learning Identification and Classification of Historic Ceramics’. Archaeology in New Zealand, 13.
    Back to article
  37. 37Bickler, SH. 2021. ‘Machine Learning Arrives in Archaeology’. Advances in Archaeological Practice, 9(2): 186–191. DOI: 10.1017/aap.2021.6
    Back to article
  38. 38Binford, LR and Binford, SR. 1966. ‘A Preliminary Analysis of Functional Variability in the Mousterian of Levallois Facies’. American Anthropologist, 68(2): 238–295.
    Back to article
  39. 39Bonhage, A, Eltaher, M, Raab, T, Breuss, M, Raab, A and Schneider, A. 2021. ‘A modified Mask region-based convolutional neural network approach for the automated detection of archaeological sites on high-resolution light detection and ranging-derived digital elevation models in the North German Lowland’. Archaeological prospection, 28(2): 177–186. DOI: 10.1002/arp.1806
    Back to article
  40. 40Boon, P, van Der Maaten, L, Paijmans, H, Postma, E and Lange, G. 2009. ‘Digital Support for Archaeology’. Interdisciplinary Science Reviews, 34(2–3): 189–205. DOI: 10.1179/174327909X441108
    Back to article
  41. 41Bordon, P, Martinelli, P, Medina, P, Bonomo, N and Ratto, N. 2021. ‘Automatic detection of mud-wall signatures in ground-penetrating radar data’. Archaeological prospection, 28(1): 89–106. DOI: 10.1002/arp.1799
    Back to article
  42. 42Boston, T, Van Dijk, A, Larraondo, P and Thackway, R. 2022. ‘Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset’. Remote Sensing, 14(14): 3396. DOI: 10.3390/rs14143396
    Back to article
  43. 43Bouzid, S and Barge, O. 2022. ‘Towards a typology of desert kites combining quantitative and spatial approaches’. Archaeological and anthropological sciences, 14(5). DOI: 10.1007/s12520-022-01551-0
    Back to article
  44. 44Brandsen, A. 2023. ‘Information Extraction and Machine Learning for Archaeological Texts’. In: Gonzalez-Perez, C, Martin-Rodilla, P and Pereira-Fariña, M (eds.) Discourse and Argumentation in Archaeology: Conceptual and Computational Approaches. Quantitative Archaeology and Archaeological Modelling. Cham: Springer International Publishing. pp. 229–261. DOI: 10.1007/978-3-031-37156-1_11
    Back to article
  45. 45Brandsen, A and Koole, M. 2022. ‘Labelling the past: data set creation and multi-label classification of Dutch archaeological excavation reports’. Language Resources and Evaluation, 56(2): 543–572. DOI: 10.1007/s10579-021-09552-6
    Back to article
  46. 46Brandsen, A and Lippok, F. 2021. ‘A burning question-Using an intelligent grey literature search engine to change our views on early medieval burial practices in the Netherlands’. Journal of Archaeological Science, 133. DOI: 10.1016/j.jas.2021.105456
    Back to article
  47. 47Brandt, R, Groenewoudt, BJ and Kvamme, KL. 1992. ‘An Experiment in Archaeological Site Location: Modeling in the Netherlands using GIS Techniques’. World Archaeology, 24(2): 268–282. DOI: 10.1080/00438243.1992.9980207
    Back to article
  48. 48Breiman, L. 1996. ‘Bagging predictors’. Machine Learning, 24(2): 123–140. DOI: 10.1007/BF00058655
    Back to article
  49. 49Brown, TB, Mann, B, Ryder, N, Subbiah, M, Kaplan, J, Dhariwal, P, Neelakantan, A, Shyam, P, Sastry, G, Askell, A, Agarwal, S, Herbert-Voss, A, Krueger, G, Henighan, T, Child, R, Ramesh, A, Ziegler, DM, Wu, J, Winter, C, Hesse, C, Chen, M, Sigler, E, Litwin, M, Gray, S, Chess, B, Clark, J, Berner, C, McCandlish, S, Radford, A, Sutskever, I and Amodei, D. 2020. Language Models are Few-Shot Learners. DOI: 10.48550/arXiv.2005.14165
    Back to article
  50. 50Bundzel, M, Jascur, M, Kovac, M, Lieskovsky, T, Sincak, P and Tkacik, T. 2020. ‘Semantic Segmentation of Airborne LiDAR Data in Maya Archaeology’. Remote Sensing, 12(22): 3685. DOI: 10.3390/rs12223685
    Back to article
  51. 51Byeon, W, Dominguez-Rodrigo, M, Arampatzis, G, Baquedano, E, Yravedra, J, Mate-Gonzalez, M and Koumoutsakos, P. 2019. ‘Automated identification and deep classification of cut marks on bones and its paleoanthropological implications’. Journal of Computational Science, 32: 36–43. DOI: 10.1016/j.jocs.2019.02.005
    Back to article
  52. 52Bzdok, D. 2017. ‘Classical Statistics and Statistical Learning in Imaging Neuroscience’. Frontiers in Neuroscience, 11: 543. DOI: 10.3389/fnins.2017.00543
    Back to article
  53. 53Bzdok, D, Altman, N and Krzywinski, M. 2018. ‘Statistics versus machine learning’. Nature Methods, 15(4): 233–234. DOI: 10.1038/nmeth.4642
    Back to article
  54. 54Cacciari, I and Pocobelli, GF. 2021. ‘The contribution of artificial intelligence to aerial photointerpretation of archaeological sites: a comparison between traditional and machine learning methods’. Archeologia e Calcolatori, 32(1): 81–98. DOI: 10.19282/ac.32.1.2021.05
    Back to article
  55. 55Cacciari, I and Pocobelli, GF. 2022. ‘Machine Learning: A Novel Tool for Archaeology’. In: D’Amico, S and Venuti, V (eds.) Handbook of Cultural Heritage Analysis. Cham: Springer International Publishing. pp. 961–1002. DOI: 10.1007/978-3-030-60016-7_33
    Back to article
  56. 56Calder, J, Coil, R, Melton, JA, Olver, PJ, Tostevin, G and Yezzi-Woodley, K. 2022. ‘Use and Misuse of Machine Learning in Anthropology’. IEEE BITS the Information Theory Magazine, 1–13. DOI: 10.1109/MBITS.2022.3205143
    Back to article
  57. 57Canul-Ku, M, Hasimoto-Beltran, R, Jimenez-Badillo, D, Ruiz-Correa, S and Roman-Rangel, E. 2019. ‘Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures’. IEEE Access, 7: 3298–3313. DOI: 10.1109/ACCESS.2018.2886791
    Back to article
  58. 58Cardarelli, L. 2024. ‘From fragments to digital wholeness: An AI generative approach to reconstructing archaeological vessels’. Journal of Cultural Heritage, 70: 250–258. DOI: 10.1016/j.culher.2024.09.012
    Back to article
  59. 59Cardarelli, L. 2025. ‘The legacy: the Data Science movement’. In: Analyse des données and archaeology fifty years later: from data analysis to data science.
    Back to article
  60. 60Carr, C. 1989. For concordance in archaeological analysis: bridging data structure, quantitative technique, and theory. Prospect Heights, IL: Waveland Press.
    Back to article
  61. 61Carroll, SR, Garba, I, Figueroa-Rodríguez, OL, Holbrook, J, Lovett, R, Materechera, S, Parsons, M, Raseroka, K, Rodriguez-Lonebear, D, Rowe, R, Sara, R, Walker, JD, Anderson, J and Hudson, M. 2020. ‘The CARE Principles for Indigenous Data Governance’. Data Science Journal, 19(1). DOI: 10.5334/dsj-2020-043
    Back to article
  62. 62Carter, B, Blackadar, J and Conner, W. 2021. ‘When Computers Dream of Charcoal Using Deep Learning, Open Tools, and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania’. Advances in archaeological practice, 9(4): 257–271. DOI: 10.1017/aap.2021.17
    Back to article
  63. 63Carvalho, DV, Pereira, EM and Cardoso, JS. 2019. ‘Machine Learning Interpretability: A Survey on Methods and Metrics’. Electronics, 8(8): 832. DOI: 10.3390/electronics8080832
    Back to article
  64. 64Casillo, M, Colace, F, Gaeta, R, Lorusso, A and Pellegrino, M. 2025. ‘Artificial Intelligence in Archaeological Site Conservation: Trends, Challenges, and Future Directions’. Journal of Computer Applications in Archaeology, 8(1). DOI: 10.5334/jcaa.207
    Back to article
  65. 65Casini, L, Marchetti, N, Montanucci, A, Orrù, V and Roccetti, M. 2023. ‘A human–AI collaboration workflow for archaeological sites detection’. Scientific Reports, 13(1): 8699. DOI: 10.1038/s41598-023-36015-5
    Back to article
  66. 66Caspari, G and Crespo, P. 2019. ‘Convolutional neural networks for archaeological site detection – Finding “princely” tombs’. Journal of Archaeological Science, 110: 104998. DOI: 10.1016/j.jas.2019.104998
    Back to article
  67. 67Castiello, ME. 2022. Computational and Machine Learning Tools for Archaeological Site Modeling. Springer Theses. Cham: Springer International Publishing. DOI: 10.1007/978-3-030-88567-0
    Back to article
  68. 68Castiello, ME and Tonini, M. 2021. ‘An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study’. Journal of Computer Applications in Archaeology, 4(1): 110–125. DOI: 10.5334/jcaa.71
    Back to article
  69. 69Chai, J, Zeng, H, Li, A and Ngai, EWT. 2021. ‘Deep learning in computer vision: A critical review of emerging techniques and application scenarios’. Machine Learning with Applications, 6: 100134. DOI: 10.1016/j.mlwa.2021.100134
    Back to article
  70. 70Chamberlain, S. 2022. habanero: Low Level Client for Crossref Search API.
    Back to article
  71. 71Chang, Y, Wang, X, Wang, J, Wu, Y, Yang, L, Zhu, K, Chen, H, Yi, X, Wang, C, Wang, Y, Ye, W, Zhang, Y, Chang, Y, Yu, PS, Yang, Q and Xie, X. 2024. ‘A Survey on Evaluation of Large Language Models’. ACM Transactions on Intelligent Systems and Technology. DOI: 10.1145/3641289
    Back to article
  72. 72Character, L, Ortiz Jr, A, Beach, T and Luzzadder-Beach, S. 2021. ‘Archaeologic Machine Learning for Shipwreck Detection Using Lidar and Sonar’. Remote Sensing, 13(9): 1759. DOI: 10.3390/rs13091759
    Back to article
  73. 73Cheng, B, Girshick, R, Dollár, P, Berg, AC and Kirillov, A. 2021. Boundary IoU: Improving Object-Centric Image Segmentation Evaluation. DOI: 10.1109/CVPR46437.2021.01508
    Back to article
  74. 74Cheng, G, Han, J and Lu, X. 2017. ‘Remote Sensing Image Scene Classification: Benchmark and State of the Art’. Proceedings of the IEEE, 105(10): 1865–1883. DOI: 10.1109/JPROC.2017.2675998
    Back to article
  75. 75Cholewiak, SA, Ipeirotis, P, Silva, V and Kannawadi, A. 2022. scholarly: Simple access to Google Scholar authors and citations.
    Back to article
  76. 76Cifuentes-Alcobendas, G and Domínguez-Rodrigo, M. 2019. ‘Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks’. Scientific Reports, 9(1): 18933. DOI: 10.1038/s41598-019-55439-6
    Back to article
  77. 77Clusmann, J, Kolbinger, FR, Muti, HS, Carrero, ZI, Eckardt, J-N, Laleh, NG, Löffler, CML, Schwarzkopf, S-C, Unger, M, Veldhuizen, GP, Wagner, SJ and Kather, JN. 2023. ‘The future landscape of large language models in medicine’. Communications Medicine, 3(1): 1–8. DOI: 10.1038/s43856-023-00370-1
    Back to article
  78. 78Cobb, PJ. 2023. ‘Large Language Models and Generative AI, Oh My!: Archaeology in the Time of ChatGPT, Midjourney, and Beyond’. Advances in Archaeological Practice, 11(3): 363–369. DOI: 10.1017/aap.2023.20
    Back to article
  79. 79Cole, KE, Yaworsky, PM and Hart, IA. 2022. ‘Evaluating statistical models for establishing morphometric taxonomic identifications and a new approach using Random Forest’. Journal of Archaeological Science, 143: 105610. DOI: 10.1016/j.jas.2022.105610
    Back to article
  80. 80Coombes, P and Barber, K. 2005. ‘Environmental Determinism in Holocene Research: Causality or Coincidence?’. Area, 37(3): 303–311
    Back to article
  81. 81Courtenay, L, Yravedra, J, Huguet, R, Aramendi, J, Mate-Gonzalez, M, Gonzalez-Aguilera, D and Arriaza, M. 2019. ‘Combining machine learning algorithms and geometric morphometrics: A study of carnivore tooth marks’. Palaeogeography Palaeoclimatology Palaeoecology, 522: 28–39. DOI: 10.1016/j.palaeo.2019.03.007
    Back to article
  82. 82Courtenay, LA, Vanderesse, N, Doyon, L and Souron, A. 2024. ‘Deep Learning-Based Computer Vision Is Not Yet the Answer to Taphonomic Equifinality in Bone Surface Modifications’. Journal of Computer Applications in Archaeology, 7(1). DOI: 10.5334/jcaa.145
    Back to article
  83. 83Čož, N, Kokalj, Ž and Kostovska, A. 2024. EarthObservation/adaf.
    Back to article
  84. 84Davis, D, Caspari, G, Lipo, C and Sanger, M. 2021. ‘Deep learning reveals extent of Archaic Native American shell-ring building practices’. Journal of Archaeological Science, 132. DOI: 10.1016/j.jas.2021.105433
    Back to article
  85. 85Davis, D and Douglass, K. 2020. ‘Aerial and Spaceborne Remote Sensing in African Archaeology: A Review of Current Research and Potential Future Avenues’. African Archaeological Review, 37(1): 9–24. DOI: 10.1007/s10437-020-09373-y
    Back to article
  86. 86Davis, D and Lundin, J. 2021. ‘Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning’. Remote Sensing, 13(18). DOI: 10.3390/rs13183680
    Back to article
  87. 87Davis, DS. 2020a. ‘Defining what we study: The contribution of machine automation in archaeological research’. Digital Applications in Archaeology and Cultural Heritage, 18: e00152. DOI: 10.1016/j.daach.2020.e00152
    Back to article
  88. 88Davis, DS. 2020b. ‘Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research’. Remote Sensing, 12(6): 921. DOI: 10.3390/rs12060921
    Back to article
  89. 89Demján, P, Dreslerová, D, Kolář, J, Chuman, T, Romportl, D, Trnka, M and Lieskovský, T. 2022b. ‘Long time-series ecological niche modelling using archaeological settlement data: Tracing the origins of present-day landscape’. Applied Geography, 141: 102669. DOI: 10.1016/j.apgeog.2022.102669
    Back to article
  90. 90Demján, P, Pavuk, P and Roosevelt, CH. 2022a. ‘Laser-Aided Profile Measurement and Cluster Analysis of Ceramic Shapes’. Journal of Field Archaeology. DOI: 10.1080/00934690.2022.2128549
    Back to article
  91. 91Dhivya, S and Devi, G. 2021. ‘TAMIZHI: Historical Tamil-Brahmi Script Recognition Using CNN and MobileNet’. ACM transactions on asian and low-resource language information processing, 20(3). DOI: 10.1145/3402891
    Back to article
  92. 92Djindjian, F. 2015. ‘A Short History of the Beginnings of Mathematics in Archaeology’. In: Barceló, JA and Bogdanovic, I (eds.) Mathematics and Archaeology. Boco Raton, FL: CRC Press. pp. 65–85. DOI: 10.1201/b18530-4
    Back to article
  93. 93Domínguez-Rodrigo, M. 2018. ‘Successful classification of experimental bone surface modifications (BSM) through machine learning algorithms: a solution to the controversial use of BSM in paleoanthropology?’. Archaeological and anthropological sciences, 11(6): 2711–2725. DOI: 10.1007/s12520-018-0684-9
    Back to article
  94. 94Domínguez-Rodrigo, M and Baquedano, E. 2018. ‘Distinguishing butchery cut marks from crocodile bite marks through machine learning methods’. Scientific Reports, 8(1): 5786. DOI: 10.1038/s41598-018-24071-1
    Back to article
  95. 95Doran, J. 1990. ‘Computer based simulation and formal modeling in archaeology: A review’. In: Mathematics and information science in archaeology: A flexible framework. Bonn: Holos. pp. 93–114.
    Back to article
  96. 96Dramsch, JS. 2020. 70 years of machine learning in geoscience in review. In: Advances in Geophysics. Elsevier. pp. 1–55. DOI: 10.1016/bs.agph.2020.08.002
    Back to article
  97. 97Eleftheriadou, A, McPherron, SP and Marreiros, J. 2025. ‘Machine Learning Applications in Use-Wear Analysis: A Critical Review’. Journal of Computer Applications in Archaeology, 8(1). DOI: 10.5334/jcaa.190
    Back to article
  98. 98El-Hajj, H. 2021. ‘Interferometric SAR and Machine Learning: Using Open Source Data to Detect Archaeological Looting and Destruction’. Journal of Computer Applications in Archaeology, 4(1): 47–62. DOI: 10.5334/jcaa.70
    Back to article
  99. 99Eloundou, T, Manning, S, Mishkin, P and Rock, D. 2023. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. DOI: 10.48550/arXiv.2303.10130
    Back to article
  100. 100Emmitt, J, Masoud-Ansari, S, Phillipps, R, Middleton, S, Graydon, J and Holdaway, S. 2022. ‘Machine learning for stone artifact identification: Distinguishing worked stone artifacts from natural clasts using deep neural networks’. PLOS One, 17(8): e0271582. DOI: 10.1371/journal.pone.0271582
    Back to article
  101. 101European Commission, Directorate-General for Research and Innovation and Directorate E-Prosperity. 2024. Living guidelines on the responsible use of generative AI in research. p. 14.
    Back to article
  102. 102Fayyad, U, Piatetsky-Shapiro, G and Smyth, P. 1996. ‘From Data Mining to Knowledge Discovery in Databases’. AI Magazine, 17(3): 37–37. DOI: 10.1609/aimag.v17i3.1230
    Back to article
  103. 103Febriawan, HK, Moefti, O, Haryanto, D and Wiguna, T. 2020. ‘Detection and characterization of an archaeological wreck site in Sunda Strait, Indonesia’. Forum geografic, XIX(1): 60–71. DOI: 10.5775/fg.2020.054.i
    Back to article
  104. 104Feinerer, I and Hornik, K. 2023. tm: Text Mining Package.
    Back to article
  105. 105Felicetti, A, Paolanti, M, Zingaretti, P, Pierdicca, R and Malinverni, E. 2021. ‘MO.SE.: Mosaic image segmentation based on Deep cascading Learning’. Virtual Archaeology Review, 12(24): 25–38. DOI: 10.4995/var.2021.14179
    Back to article
  106. 106Fernée, CL and Trimmis, KP. 2022. ‘The rolling stones of Bronze Age Aegean: Applying machine learning to explore the use of lithic spheres from Akrotiri, Thera’. Journal of Archaeological Science: Reports, 45: 103615. DOI: 10.1016/j.jasrep.2022.103615
    Back to article
  107. 107Field, A, Miles, J and Field, Z. 2012. Discovering statistics using R. Los Angeles, London, New Delhi, Singapore, Washington, DC: Sage.
    Back to article
  108. 108Fiorucci, M, Khoroshiltseva, M, Pontil, M, Traviglia, A, Del Bue, A and James, S. 2020. ‘Machine Learning for Cultural Heritage: A Survey’. Pattern Recognition Letters, 133: 102–108. DOI: 10.1016/j.patrec.2020.02.017
    Back to article
  109. 109Fiorucci, M, Verschoof-van der Vaart, WB, Soleni, P, Le Saux, B and Traviglia, A. 2022. ‘Deep Learning for Archaeological Object Detection on LiDAR: New Evaluation Measures and Insights’. Remote Sensing, 14(7): 1694. DOI: 10.3390/rs14071694
    Back to article
  110. 110Fisher, M, Fradley, M, Flohr, P, Rouhani, B and Simi, F. 2021. ‘Ethical considerations for remote sensing and open data in relation to the endangered archaeology in the Middle East and North Africa project’. Archaeological Prospection, 28(3): 279–292. DOI: 10.1002/arp.1816
    Back to article
  111. 111Fisher, MT, Jurkenas, D, Jambajantsan, A, Jamsranjav, B, Nasan-Ochir, E-O, Gelegdorj, E, Chuluunbat, M, Petraglia, M and Boivin, N. 2022. ‘Multidisciplinary digital methodologies for documentation and preservation of immovable Archaeological heritage in the Khovd River Valley, Western Mongolia’. F1000Research, 11(1250).
    Back to article
  112. 112Friggens, MM, Loehman, RA, Constan, CI and Kneifel, RR. 2021. ‘Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA’. Fire Ecology, 17(1): 18. DOI: 10.1186/s42408-021-00103-6
    Back to article
  113. 113Gallwey, J, Eyre, M, Tonkins, M and Coggan, J. 2019. ‘Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning’. Remote Sensing, 11(17). DOI: 10.3390/rs11171994
    Back to article
  114. 114Gansell, A, van de Meent, J, Zairis, S and Wiggins, C. 2014. ‘Stylistic clusters and the Syrian/South Syrian tradition of first-millennium BCE Levantine ivory carving: a machine learning approach’. Journal of Archaeological Science, 44: 194–205. DOI: 10.1016/j.jas.2013.11.005
    Back to article
  115. 115Garcia-Molsosa, A, Orengo, H, Lawrence, D, Philip, G, Hopper, K and Petrie, C. 2021. ‘Potential of deep learning segmentation for the extraction of archaeological features from historical map series’. Archaeological prospection, 28(2): 187–199. DOI: 10.1002/arp.1807
    Back to article
  116. 116Gattiglia, G. 2025. ‘Managing Artificial Intelligence in Archeology. An overview’. Journal of Cultural Heritage, 71: 225–233. DOI: 10.1016/j.culher.2024.11.020
    Back to article
  117. 117Gillings, M, Hacigüzeller, P and Lock, GR. (eds.) 2020. Archaeological spatial analysis: a methodological guide. New York: Routledge. DOI: 10.4324/9781351243858
    Back to article
  118. 118Ginau, A, Steiniger, D, Hartmann, R, Hartung, U, Schiestl, R, Altmeyer, M, Seeliger, M and Wunderlich, J. 2020. ‘What settlements leave behind – pXRF compositional data analysis of archaeological layers from Tell el-Fara’in (Buto, Egypt) using machine learning’. Palaeogeography Palaeoclimatology Palaeoecology, 546. DOI: 10.1016/j.palaeo.2020.109666
    Back to article
  119. 119González-Molina, I, Jiménez-García, B, Maíllo-Fernández, J-M, Baquedano, E and Domínguez-Rodrigo, M. 2020. ‘Distinguishing Discoid and Centripetal Levallois methods through machine learning’. PLOS One, 15(12): e0244288. DOI: 10.1371/journal.pone.0244288
    Back to article
  120. 120Gonzalez-Perez, C, Martin-Rodilla, P and Pereira-Fariña, M. (eds.) 2023. Discourse and Argumentation in Archaeology: Conceptual and Computational Approaches. Quantitative Archaeology and Archaeological Modelling. Cham: Springer International Publishing. DOI: 10.1007/978-3-031-37156-1
    Back to article
  121. 121Graham, S, Huffer, D and Blackadar, J. 2020. ‘Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram’. Heritage, 3(2): 208–227. DOI: 10.3390/heritage3020013
    Back to article
  122. 122Grant, MJ and Booth, A. 2009. ‘A typology of reviews: an analysis of 14 review types and associated methodologies’. Health Information and Libraries Journal, 26(2): 91–108. DOI: 10.1111/j.1471-1842.2009.00848.x
    Back to article
  123. 123Grilli, E, Dininno, D, Marsicano, L, Petrucci, G and Remondino, F. 2018. ‘Supervised segmentation of 3D cultural heritage’. In: Addison, A and Thwaites, H (eds.) 2018 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 2018 24th International Conference on Virtual Systems & Multimedia (VSMM 2018). 2018. San Francisco, CA, USA. pp. 467–474. DOI: 10.1109/DigitalHeritage.2018.8810107
    Back to article
  124. 124Grilli, E and Remondino, F. 2019. ‘Classification of 3D Digital Heritage’. Remote Sensing, 11(7). DOI: 10.3390/rs11070847
    Back to article
  125. 125Gualandi, ML, Gattiglia, G and Anichini, F. 2021. ‘An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms’. Heritage, 4(1): 140–159. DOI: 10.3390/heritage4010008
    Back to article
  126. 126Guo, Y, Liu, Y, Oerlemans, A, Lao, S, Wu, S and Lew, MS. 2016. ‘Deep learning for visual understanding: A review’. Neurocomputing, 187: 27–48. DOI: 10.1016/j.neucom.2015.09.116
    Back to article
  127. 127Gupta, N, Martindale, A, Supernant, K and Elvidge, M. 2023. ‘The CARE Principles and the Reuse, Sharing, and Curation of Indigenous Data in Canadian Archaeology’. Advances in Archaeological Practice, 11(1): 76–89. DOI: 10.1017/aap.2022.33
    Back to article
  128. 128Guyot, A, Hubert-Moy, L and Lorho, T. 2018. ‘Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques’. Remote Sensing, 10(2): 225. DOI: 10.3390/rs10020225
    Back to article
  129. 129Guyot, A, Lennon, M, Lorho, T and Hubert-Moy, L. 2021. ‘Combined Detection and Segmentation of Archeological Structures from LiDAR Data Using a Deep Learning Approach’. Journal of Computer Applications in Archaeology, 4(1): 1. DOI: 10.5334/jcaa.64
    Back to article
  130. 130Haby, MM, Chapman, E, Clark, R, Barreto, J, Reveiz, L and Lavis, JN. 2016. ‘What are the best methodologies for rapid reviews of the research evidence for evidence-informed decision making in health policy and practice: a rapid review’. Health Research Policy and Systems, 14(1): 83. DOI: 10.1186/s12961-016-0155-7
    Back to article
  131. 131Hagendorff, T. 2024. ‘Mapping the Ethics of Generative AI: A Comprehensive Scoping Review’. Minds and Machines, 34(4): 39. DOI: 10.1007/s11023-024-09694-w
    Back to article
  132. 132Hansen, J and Nebel, M. 2020. ‘Prioritizing Archaeological Inventory and Protection with Predictive Probability Models at Glen Canyon National Recreation Area, USA’. Kiva-Journal of Southwestern Anthropology and History, 86(1): 1–23. DOI: 10.1080/00231940.2019.1684003
    Back to article
  133. 133Hastie, T, Tibshirani, R and Friedman, J. 2009. ‘The Elements of Statistical Learning’. Springer Series in Statistics. New York,, NY: Springer New York. DOI: 10.1007/978-0-387-84858-7
    Back to article
  134. 134He, K, Zhang, X, Ren, S and Sun, J. 2016. ‘Deep Residual Learning for Image Recognition’. In: 2016. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2016. pp. 770–778. DOI: 10.1109/CVPR.2016.90
    Back to article
  135. 135Herrault, P, Poterek, Q, Keller, B, Schwartz, D and Ertlen, D. 2021. ‘Automated detection of former field systems from airborne laser scanning data: a new approach for Historical Ecology’. International Journal of Applied Earth Observation and Geoinformation, 104. DOI: 10.1016/j.jag.2021.102563
    Back to article
  136. 136Hodder, I. 1986. Reading the Past. 1st edition. Cambridge, England; New York: Cambridge University Press.
    Back to article
  137. 137Hodson, FR. 1970. ‘Cluster analysis and archaeology: Some new developments and applications’. World Archaeology, 1: 299–320.
    Back to article
  138. 138Holt, E, Evans, JA and Madgwick, R. 2021. ‘Strontium (Sr-87/Sr-86) mapping: A critical review of methods and approaches’. Earth-Science Reviews, 216: 103593. DOI: 10.1016/j.earscirev.2021.103593
    Back to article
  139. 139Horn, C, Green, A, Skaerstroem, V, Lindhe, C, Peternell, M and Ling, J. 2022a. ‘A Boat Is a Boat Is a Boat horizontal ellipsis Unless It Is a Horse – Rethinking the Role of Typology’. Open Archaeology, 8(1): 1218–1230. DOI: 10.1515/opar-2022-0277
    Back to article
  140. 140Horn, C, Ivarsson, O, Lindhe, C, Potter, R, Green, A and Ling, J. 2022b. ‘Artificial Intelligence, 3D Documentation, and Rock Art-Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images’. Journal of Archaeological Method and Theory, 29(1): 188–213. DOI: 10.1007/s10816-021-09518-6
    Back to article
  141. 141Hörr, C. 2011. Algorithmen zur automatisierten Dokumentation und Klassifikation archäologischer Gefäße. Chemnitz: Universitätsverlag Chemnitz.
    Back to article
  142. 142Hörr, C, Lindinger, E and Brunnett, G. 2014. ‘Machine Learning Based Typology Development in Archaeology’. ACM Journal on Computing and Cultural Heritage, 7(1): 2. DOI: 10.1145/2533988
    Back to article
  143. 143Huang, L, Perry, PO, Årup Nielsen, F, Porter, M and Boulton, R. 2021. corpus: Text Corpus Analysis.
    Back to article
  144. 144Huggett, J. 2017. ‘The Apparatus of Digital Archaeology’. Internet Archaeology, (44). DOI: 10.11141/ia.44.7
    Back to article
  145. 145Ionescu, V-S. 2015. ‘Applying Support Vector Regression Methods for Height Estimation in Archaeology’. Studia Universitatis Babeş-Bolyai Informatica, 70(2): 70–82.
    Back to article
  146. 146Jalandoni, A, Zhang, Y and Zaidi, N. 2022. ‘On the use of Machine Learning methods in rock art research with application to automatic painted rock art identification’. Journal of Archaeological Science, 144. DOI: 10.1016/j.jas.2022.105629
    Back to article
  147. 147Jamil, AH, Yakub, F, Azizan, A, Roslan, SA, Zaki, SA and Ahmad, SA. 2022. ‘A Review on Deep Learning Application for Detection of Archaeological Structures’. Journal of Advanced Research in Applied Sciences and Engineering Technology, 26(1): 7–14. DOI: 10.37934/araset.26.1.714
    Back to article
  148. 148Janzen, A, Bataille, C, Copeland, SR, Quinn, RL, Ambrose, SH, Reed, D, Hamilton, M, Grimes, V, Richards, MP, le Roux, P and Roberts, P. 2020. ‘Spatial variation in bioavailable strontium isotope ratios (87Sr/86Sr) in Kenya and northern Tanzania: Implications for ecology, paleoanthropology, and archaeology’. Palaeogeography, Palaeoclimatology, Palaeoecology, 560: 109957. DOI: 10.1016/j.palaeo.2020.109957
    Back to article
  149. 149Jesson, JK, Matheson, L and Lacey, FM. 2012. Doing your literature review: traditional and systematic techniques. Repr. Los Angeles, Calif.: Sage.
    Back to article
  150. 150Judge, J and Sebastian, L. 1988. Quantifying the present and predicting the past : theory, method, and application of archaeological predictive modeling. Denver: U.S. Department of the Interior, Bureau of Land Management Service Center.
    Back to article
  151. 151Kawamleh, S. 2024. ‘Algorithmic evidence in U.S criminal sentencing’. AI and Ethics. DOI: 10.1007/s43681-024-00473-y.
    Back to article
  152. 152Kelly, RL and Thomas, DH. 2017. Archaeology. Seventh edition. Boston, MA: Cengage Learning.
    Back to article
  153. 153Klassen, S, Weed, J and Evans, D. 2018. ‘Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia’. PLOS One, 13(11): e0205649. DOI: 10.1371/journal.pone.0205649
    Back to article
  154. 154Kochkov, D, Yuval, J, Langmore, I, Norgaard, P, Smith, J, Mooers, G, Klöwer, M, Lottes, J, Rasp, S, Düben, P, Hatfield, S, Battaglia, P, Sanchez-Gonzalez, A, Willson, M, Brenner, MP and Hoyer, S. 2024. ‘Neural general circulation models for weather and climate’. Nature, 632(8027): 1060–1066. DOI: 10.1038/s41586-024-07744-y
    Back to article
  155. 155Kogou, S, Shahtahmassebi, G, Lucian, A, Liang, H, Shui, B, Zhang, W, Su, B and van Schaik, S. 2020. ‘From remote sensing and machine learning to the history of the Silk Road: large scale material identification on wall paintings’. Scientific Reports, 10(1): 19312. DOI: 10.1038/s41598-020-76457-9
    Back to article
  156. 156Kohler, TA. 1988. ‘Predictive locational modeling: History and current practice’. In: Judge, J and Sebastian, L (eds.) Quantifying the present and predicting the past : theory, method, and application of archaeological predictive modeling. Denver: U.S. Department of the Interior, Bureau of Land Management Service Center. pp. 19–59.
    Back to article
  157. 157Kowalski, BR, Schatzki, TF and Stross, FH. 1972. ‘Classification of archaeological artifacts by applying pattern recognition to trace element data’. Analytical Chemistry, 44(13): 2176–2180. DOI: 10.1021/ac60321a002
    Back to article
  158. 158Kowlessar, J, Keal, J, Wesley, D, Moffat, I, Lawrence, D, Weson, A, Nayinggul, A, and Mimal Land Management Aboriginal Corporation. 2021. ‘Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia’. Australian Archaeology, 87(2): 115–126. DOI: 10.1080/03122417.2021.1895481
    Back to article
  159. 159Kristiansen, K. 2019. ‘Who is deterministic? On the nature of interdisciplinary research in archaeology’. Archaeological Dialogues, 26(01): 12–14. DOI: 10.1017/S1380203819000060
    Back to article
  160. 160Kubat, M. 2017. An Introduction to Machine Learning. Cham: Springer International Publishing. DOI: 10.1007/978-3-319-63913-0
    Back to article
  161. 161Kvamme, KL. 2006. ‘There and Back Again: Revisiting Archaeologial Locational Modeling’. In: Mehrer, M and Wescott, K (eds.) GIS and archaeological site location modeling. Boca Raton, FL: Taylor & Francis. pp. 3–38. DOI: 10.1201/9780203563359.sec1
    Back to article
  162. 162Labba, C, Alcouffe, A, Crubézy, E and Boyer, A. 2023. ‘IArch: An AI Tool for Digging Deeper into Archaeological Data’. In: 2023. IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). November 2023. pp. 22–29. DOI: 10.1109/ICTAI59109.2023.00012
    Back to article
  163. 163Landa, V, Shapira, Y, David, M, Karasik, A, Weiss, E, Reuveni, Y and Drori, E. 2021. ‘Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method’. Scientific Reports, 11(1): 13577. DOI: 10.1038/s41598-021-92559-4
    Back to article
  164. 164Lapp, E and Lapp, L. 2024. ‘Evaluating ChatGPT as a viable research tool for typological investigations of cultural heritage artefacts-Roman clay oil lamps’. Archaeometry, 66(3): 696–717. DOI: 10.1111/arcm.12937
    Back to article
  165. 165Leroi-Gourhan, A. 2022. Le geste et la parole. Espaces libres. 2nd ed. Paris: Albin Michel.
    Back to article
  166. 166Li, G, Dong, J, Che, M, Wang, X, Fan, J and Dong, G. 2024. ‘GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau’. Remote Sensing, 16(8). DOI: 10.3390/rs16081454
    Back to article
  167. 167Lin, T-Y, Maire, M, Belongie, S, Hays, J, Perona, P, Ramanan, D, Dollár, P and Zitnick, CL. 2014. ‘Microsoft COCO: Common Objects in Context’. In: Fleet, D, Pajdla, T, Schiele, B and Tuytelaars, T (eds.) Computer Vision – ECCV 2014. 2014. Cham: Springer International Publishing. pp. 740–755. DOI: 10.1007/978-3-319-10602-1_48
    Back to article
  168. 168Ling, Z, Delnevo, G, Salomoni, P and Mirri, S. 2024. ‘Findings on Machine Learning for Identification of Archaeological Ceramics: A Systematic Literature Review’. IEEE Access, 12: 100167–100185. DOI: 10.1109/ACCESS.2024.3429623
    Back to article
  169. 169Liu, V, Long, T, Raw, N and Chilton, L. 2023. Generative Disco: Text-to-Video Generation for Music Visualization. DOI: 10.48550/arXiv.2304.08551
    Back to article
  170. 170Lock, G and Harris, T. 2006. ‘Enhancing Predictive Archaeological Modeling: Integrating Location, Landscape, and Culture’. In: Mehrer, M and Wescott, K (eds.) GIS and archaeological site location modeling. Boca Raton, FL: Taylor & Francis. pp. 41–62. DOI: 10.1201/9780203563359.sec2
    Back to article
  171. 171Long, Y, Xia, G-S, Li, S, Yang, W, Yang, MY, Zhu, XX, Zhang, L and Li, D. 2021. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID. DOI: 10.48550/arXiv.2006.12485
    Back to article
  172. 172Lundberg, SM and Lee, S-I. 2017. ‘A unified approach to interpreting model predictions’. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17. décembre 2017. Red Hook, NY, USA: Curran Associates Inc. pp. 4768–4777.
    Back to article
  173. 173Lundström, V, Simpson, D and Yaworsky, P. 2024. ‘Here by the Sea and Sand: Uninterrupted Hunter-Fisher-Gatherer Coastal Habitation Despite Considerable Population Growth’. Open Quaternary, 10(1). DOI: 10.5334/oq.129
    Back to article
  174. 174Lyons, M, Fecher, F and Reindel, M. 2022. ‘From LiDAR to deep learning: A case study of computer-assisted approaches to the archaeology of Guadalupe and northeast Honduras’. IT-Information Technology. DOI: 10.1515/itit-2022-0004
    Back to article
  175. 175MacLeod, N. 2018. ‘The quantitative assessment of archaeological artifact groups: Beyond geometric morphometrics’. Quaternary Science Reviews, 201: 319–348. DOI: 10.1016/j.quascirev.2018.08.024
    Back to article
  176. 176Ma, Y, Grimes, V, Van Biesen, G, Shi, L, Chen, K, Mannino, M and Fuller, B. 2021. ‘Aminoisoscapes and palaeodiet reconstruction: New perspectives on millet-based diets in China using amino acid delta C-13 values’. Journal of Archaeological Science, 125. DOI: 10.1016/j.jas.2020.105289
    Back to article
  177. 177MAIA. 2025. ‘Managing Artificial Intelligence in Archaeology’. https://maiacost.eu/.
    Back to article
  178. 178Mantovan, L and Nanni, L. 2020. ‘The Computerization of Archaeology: Survey on Artificial Intelligence Techniques’. SN Computer Science, 1(5): 267. DOI: 10.1007/s42979-020-00286-w
    Back to article
  179. 179Marom, N. 2025. ‘Current methods and theory in quantitative zooarchaeology’. Journal of Archaeological Science, 176: 106165. DOI: 10.1016/j.jas.2025.106165
    Back to article
  180. 180Martin-Perea, D, Courtenay, L, Domingo, M and Morales, J. 2020. ‘Application of artificially intelligent systems for the identification of discrete fossiliferous levels’. PEERJ, 8. DOI: 10.7717/peerj.8767
    Back to article
  181. 181Marwick, B, Barton, CM, Bates, L, Bollwerk, E, Bocinsky, K, Carter, AK, Conrad, C, Costa, S, Crema, ER, Davies, B, Drake, L, Dye, TS, Giusti, D, Graham, S, Hawks, J, Huffer, D, Madsen, ME, Neiman, FD, Opitz, R, Riel-Salvatore, J, Riris, P, Romanowska, I, Ullah, I and Wren, CD. 2017. Open Science in Archaeology. DOI: 10.31235/osf.io/72n8g
    Back to article
  182. 182Marwick, B and Birch, SEP. 2018. ‘A Standard for the Scholarly Citation of Archaeological Data as an Incentive to Data Sharing’. Advances in Archaeological Practice, 6(2): 125–143. DOI: 10.1017/aap.2018.3
    Back to article
  183. 183Massachusetts Institute of Technology. 2024. Initial guidance for use of Generative AI tools. Information Systems & Technology, 4 March 2024. Available at https://ist.mit.edu/ai-guidance [Last accessed 16 December 2024].
    Back to article
  184. 184Matrone, F and Martini, M. 2021. ‘Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds’. Virtual Archaeology Review, 12(25): 73–84. DOI: 10.4995/var.2021.15318
    Back to article
  185. 185McPherron, SP, Archer, W, Otárola-Castillo, ER, Torquato, MG and Keevil, TL. 2022. ‘Machine learning, bootstrapping, null models, and why we are still not 100% sure which bone surface modifications were made by crocodiles’. Journal of human evolution, 164: 103071. DOI: 10.1016/j.jhevol.2021.103071
    Back to article
  186. 186Menze, BH and Ur, JA. 2012. ‘Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale’. Proceedings of the National Academy of Sciences, 109(14). DOI: 10.1073/pnas.1115472109
    Back to article
  187. 187Menze, BH, Ur, JA and Sherratt, AG. 2006. ‘Detection of Ancient Settlement Mounds’. Photogrammetric Engineering & Remote Sensing, 72(3): 321–327. DOI: 10.14358/PERS.72.3.321
    Back to article
  188. 188Mesanza-Moraza, A, Garcia-Gomez, I and Azkarate, A. 2021. ‘Machine Learning for the Built Heritage Archaeological Study’. ACM Journal on Computing and Cultural Heritage, 14(1): 10. DOI: 10.1145/3422993
    Back to article
  189. 189Miera, JJ, Schmidt, K, von Suchodoletz, H, Ulrich, M, Werther, L, Zielhofer, C, Ettel, P and Veit, U. 2022. ‘Large-scale investigations of Neolithic settlement dynamics in Central Germany based on machine learning analysis: A case study from the Weiße Elster river catchment’. PLOS One, 17(4): e0265835. DOI: 10.1371/journal.pone.0265835
    Back to article
  190. 190Mircea, I-G, Czibula, G and Petrușel, M-R. 2015b. ‘Sex Identification in Archaeological Remains Using Decision Tree Learning’. Studia Universitatis Babeş-Bolyai Informatica, 60(2): 91–103.
    Back to article
  191. 191Mircea, I-G, Limboi, S-G and Petrușel, M-R. 2015a. ‘A New Unsupervised Learning Based Approach for Gender Detection of Human Archaeological Remains’. Studia Universitatis Babeş-Bolyai Informatica, 60(2): 5–20.
    Back to article
  192. 192Moclán, A and Domínguez-Rodrigo, M. 2023. ‘Are highly accurate models of agency in bone breaking the result of misuse of machine learning methods?’. Journal of Archaeological Science-Reports, 51. DOI: 10.1016/j.jasrep.2023.104150
    Back to article
  193. 193Moclán, A, Domínguez-Rodrigo, M and Yravedra, J. 2019. ‘Classifying agency in bone breakage: an experimental analysis of fracture planes to differentiate between hominin and carnivore dynamic and static loading using machine learning (ML) algorithms’. Archaeological and Anthropological Sciences, 11(9): 4663–4680. DOI: 10.1007/s12520-019-00815-6
    Back to article
  194. 194Moclán, A, Huguet, R, Marquez, B, Laplana, C, Arsuaga, J, Perez-Gonzalez, A and Baquedano, E. 2020. ‘Identifying the bone-breaker at the Navalmaillo Rock Shelter (Pinilla del Valle, Madrid) using machine learning algorithms’. Archaeological and Anthropological Sciences, 12(2). DOI: 10.1007/s12520-020-01017-1
    Back to article
  195. 195Monna, F, Magail, J, Rolland, T, Navarro, N, Wilczek, J, Gantulga, J-O, Esin, Y, Granjon, L, Allard, A-C and Chateau-Smith, C. 2020. ‘Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongolia -’. Journal of Cultural Heritage, 43: 118–128. DOI: 10.1016/j.culher.2020.01.002
    Back to article
  196. 196Mozilla. 2023. geckodriver: Proxy for using W3C WebDriver-compatible clients to interact with Gecko-based browsers.
    Back to article
  197. 197Muzzall, E. 2021. ‘A Novel Ensemble Machine Learning Approach for Bioarchaeological Sex Prediction’. Technologies, 9(2): 23. DOI: 10.3390/technologies9020023
    Back to article
  198. 198Naso, M and Sciuto, C. 2025. State of the art on enhanced digitisation. p. 50.
    Back to article
  199. 199Neri, V and Dadà, S. 2025. Ethical guidelines for trustworthy AI. p. 13.
    Back to article
  200. 200Nguifo, E, Lagrange, M, Renaud, M and Sallantin, J. 1997. ‘PLATA: An application of LEGAL, a machine learning based system, to a typology of archaeological ceramics’. Computers and the humanities, 31(3): 169–187. DOI: 10.1023/A:1000904004065
    Back to article
  201. 201Nicholson, C, Kansa, S, Gupta, N and Fernandez, R. 2023. ‘Will It Ever Be FAIR?: Making Archaeological Data Findable, Accessible, Interoperable, and Reusable’. Advances in Archaeological Practice, 11(1): 63–75. DOI: 10.1017/aap.2022.40
    Back to article
  202. 202Nogales, A, Delgado-Martos, E, Melchor, A and Garcia-Tejedor, A. 2021. ‘ARQGAN: An evaluation of generative adversarial network approaches for automatic virtual inpainting restoration of Greek temples’. Expert systems with applications, 180. DOI: 10.1016/j.eswa.2021.115092
    Back to article
  203. 203Orellana Figueroa. 2020. Google Scholar Scraper.
    Back to article
  204. 204Orellana Figueroa, J, Reeves, J, McPherron, S and Tennie, C. 2021. ‘A proof of concept for machine learning-based virtual knapping using neural networks’. Scientific Reports, 11(1). DOI: 10.1038/s41598-021-98755-6
    Back to article
  205. 205Orellana Figueroa, J, Reeves, J, McPherron, S and Tennie, C. in press. Virtual Knapping (and Refitting) with Neural Networks: Proofs of Concept. In: Kyriakidis, P, Agapiou, A and Leventis, G (eds.) CAA2021. Digital Crossroads. Proceedings of the 48th Conference on Computer Applications and Quantitative Methods in Archaeology. in press. DOI: 10.1038/s41598-021-98755-6
    Back to article
  206. 206Orengo, HA, Conesa, FC, Garcia-Molsosa, A, Lobo, A, Green, AS, Madella, M and Petrie, CA. 2020. ‘Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data’. Proceedings of the National Academy of Sciences of the United States of America, 117(31): 18240-18250. DOI: 10.1073/pnas.2005583117
    Back to article
  207. 207Orengo, HA and Garcia-Molsosa, A. 2019. ‘A brave new world for archaeological survey: Automated machine learning-based potsherd detection using high-resolution drone imagery’. Journal of Archaeological Science, 112: 105013. DOI: 10.1016/j.jas.2019.105013
    Back to article
  208. 208Orton, C. 1980. Mathematics in archaeology. Collins archaeology 3. London: Collins.
    Back to article
  209. 209Osco, LP, Marcato Junior, J, Marques Ramos, AP, de Castro Jorge, LA, Fatholahi, SN, de Andrade Silva, J, Matsubara, ET, Pistori, H, Gonçalves, WN and Li, J. 2021. ‘A review on deep learning in UAV remote sensing’. International Journal of Applied Earth Observation and Geoinformation, 102: 102456. DOI: 10.1016/j.jag.2021.102456
    Back to article
  210. 210Oxford University. 2024. Guidelines on the use of generative AI. 20 February 2024. Available at https://communications.admin.ox.ac.uk/communications-resources/ai-guidance [Last accessed 16 December 2024].
    Back to article
  211. 211Padarian, J, Minasny, B and McBratney, AB. 2020. ‘Machine learning and soil sciences: a review aided by machine learning tools’. Soil, 6(1): 35–52. DOI: 10.5194/soil-6-35-2020
    Back to article
  212. 212Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, Shamseer, L, Tetzlaff, JM, Akl, EA, Brennan, SE, Chou, R, Glanville, J, Grimshaw, JM, Hróbjartsson, A, Lalu, MM, Li, T, Loder, EW, Mayo-Wilson, E, McDonald, S, McGuinness, LA, Stewart, LA, Thomas, J, Tricco, AC, Welch, VA, Whiting, P and Moher, D. 2021. ‘The PRISMA 2020. statement: an updated guideline for reporting systematic reviews’. BMJ, n71. DOI: 10.1136/bmj.n71
    Back to article
  213. 213Palacios, O. 2023. ‘Aplicación del aprendizaje automático en Arqueología: ¿Un cambio de paradigma?’. Vegueta: Anuario de la Facultad de Geografía e Historia, 147–186. DOI: 10.51349/veg.2023.1.06
    Back to article
  214. 214Pandas development team. 2022. pandas: Powerful data structures for data analysis, time series, and statistics. DOI: 10.5281/zenodo.7344967
    Back to article
  215. 215Pangti, R, Mathur, J, Chouhan, V, Kumar, S, Rajput, L, Shah, S, Gupta, A, Dixit, A, Dholakia, D, Gupta, S, Gupta, S, George, M, Sharma, VK and Gupta, S. 2021. ‘A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases’. Journal of the European Academy of Dermatology and Venereology, 35(2): 536–545. DOI: 10.1111/jdv.16967
    Back to article
  216. 216Pargeter, J, Khreisheh, N and Stout, D. 2019. ‘Understanding stone tool-making skill acquisition: Experimental methods and evolutionary implications’. Journal of Human Evolution, 133: 146–166. DOI: 10.1016/j.jhevol.2019.05.010
    Back to article
  217. 217Parsons, S. 2023. ‘Hard-Hearted Scrolls: A Noninvasive Method for Reading the Herculaneum Papyri’. Theses and Dissertations—Computer Science. DOI: 10.13023/etd.2023.372
    Back to article
  218. 218Pavan Kumar, MP, Poornima, B, Nagendraswamy, HS, Manjunath, C, Rangaswamy, BE, Varsha, M and Vinutha, HP. 2022. ‘Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches’. SN Computer Science, 3(1): 87. DOI: 10.1007/s42979-021-00935-8
    Back to article
  219. 219Pawlowicz, L and Downum, C. 2021. ‘Applications of deep learning to decorated ceramic typology and classification: A case study using Tusayan White Ware from Northeast Arizona’. Journal of Archaeological Science, 130. DOI: 10.1016/j.jas.2021.105375
    Back to article
  220. 220Pepe, M, Alfio, VS, Costantino, D and Scaringi, D. 2022. ‘Data for 3D reconstruction and point cloud classification using machine learning in cultural heritage environment’. Data in Brief, 42: 108250. DOI: 10.1016/j.dib.2022.108250
    Back to article
  221. 221Perreault, C. 2019. The quality of the archaeological record. Chicago London: The University of Chicago Press.
    Back to article
  222. 222Peters, MDJ, Godfrey, CM, Khalil, H, McInerney, P, Parker, D and Soares, CB. 2015. ‘Guidance for conducting systematic scoping reviews’. International Journal of Evidence-Based Healthcare, 13(3): 141–146. DOI: 10.1097/XEB.0000000000000050
    Back to article
  223. 223Petticrew, M and Roberts, H. 2006. Systematic Reviews in the Social Sciences: A Practical Guide. 1st ed. Wiley. DOI: 10.1002/9780470754887
    Back to article
  224. 224Phillips, P and Willey, GR. 1953. ‘Method and Theory in American Archeology: An Operational Basis for Culture-Historical Integration’. American Anthropologist, 55(5): 615–633. DOI: 10.1525/aa.1953.55.5.02a00030
    Back to article
  225. 225Phillips, SJ, Anderson, RP and Schapire, RE. 2006. ‘Maximum entropy modeling of species geographic distributions’. Ecological Modelling, 190(3–4): 231–259. DOI: 10.1016/j.ecolmodel.2005.03.026
    Back to article
  226. 226Prasomphan, S. 2022. ‘Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image’. Computer systems science and engineering, 44(2): 1295–1307. DOI: 10.32604/csse.2023.025293
    Back to article
  227. 227Python Software Foundation. 2022. Python Language Reference.
    Back to article
  228. 228R Core Team, Venables, WN and Smith, DM. 2024. An introduction to R: a programming environment for data analysis and graphics, version 4.2.1. 4th ed.
    Back to article
  229. 229Radford, J and Joseph, K. 2020. ‘Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science’. Frontiers in Big Data, 3: 18. DOI: 10.3389/fdata.2020.00018
    Back to article
  230. 230Ramazzotti, M. 2020. ‘Modeling the past. Logics, semantics and neural computing in archaeology’. Archeologia E Calcolatori, 31(2): 169–180. DOI: 10.19282/ac.31.2.2020.16
    Back to article
  231. 231Read, DW. 2018. ‘Archaeological Classification’. In: The Encyclopedia of Archaeological Sciences. John Wiley & Sons, Ltd. pp. 1–4. DOI: 10.1002/9781119188230.saseas0025
    Back to article
  232. 232Reese, K. 2021. ‘Deep learning artificial neural networks for non-destructive archaeological site dating’. Journal of Archaeological Science, 132. DOI: 10.1016/j.jas.2021.105413
    Back to article
  233. 233Renfrew, C and Bahn, PG. 2020. Archaeology: theories, methods and practice. Eighth edition, revised & updated. London: Thames & Hudson.
    Back to article
  234. 234Ribeiro, MT, Singh, S and Guestrin, C. 2016. ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. août 2016. New York, NY, USA: Association for Computing Machinery. pp. 1135–1144. DOI: 10.1145/2939672.2939778
    Back to article
  235. 235Richards, J, Tudhope, D and Vlachidis, A. 2015. ‘Text Mining in Archaeology: Extracting Information from Archaeological Reports’. In: Barceló, JA and Bogdanovic, I (eds.) Mathematics and Archaeology. CRC Press. pp. 240–254. DOI: 10.1201/b18530-17
    Back to article
  236. 236Rosenthal, R. 1979. ‘The file drawer problem and tolerance for null results’. Psychological Bulletin, 86(3): 638–641. DOI: 10.1037/0033-2909.86.3.638
    Back to article
  237. 237Ruschioni, G, Malchiodi, D, Zanaboni, A and Bonizzoni, L. 2023. ‘Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations’. Journal of Archaeological Science-Reports, 49. DOI: 10.1016/j.jasrep.2023.103995
    Back to article
  238. 238Russakovsky, O, Deng, J, Su, H, Krause, J, Satheesh, S, Ma, S, Huang, Z, Karpathy, A, Khosla, A, Bernstein, M, Berg, AC and Fei-Fei, L. 2015. ‘ImageNet Large Scale Visual Recognition Challenge’. International Journal of Computer Vision, 115(3): 211–252. DOI: 10.1007/s11263-015-0816-y
    Back to article
  239. 239Sagasti, FR. 1973. ‘Underdevelopment, Science and Technology: The Point of View of the Underdeveloped Countries’. Science Studies, 3(1): 47–59. DOI: 10.1177/030631277300300104
    Back to article
  240. 240Santos, J, Nunes, DAP, Padnevych, R, Quaresma, JC, Lopes, M, Gil, J, Bernardes, JP and Casimiro, TM. 2024. ‘Automatic ceramic identification using machine learning. Lusitanian amphorae and Faience. Two Portuguese case studies’. STAR: Science & Technology of Archaeological Research, 10(1): e2343214. DOI: 10.1080/20548923.2024.2343214
    Back to article
  241. 241Sarker, IH. 2021. ‘Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions’. SN Computer Science, 2(6): 420. DOI: 10.1007/s42979-021-00815-1
    Back to article
  242. 242Serna, A, Prates, L, Mange, E, Salazar-Garcia, DC and Bataille, CP. 2020. ‘Implications for paleomobility studies of the effects of quaternary volcanism on bioavailable strontium: A test case in North Patagonia (Argentina)’. Journal of Archaeological Science, 121: 105198. DOI: 10.1016/j.jas.2020.105198
    Back to article
  243. 243Sevilla, J, Heim, L, Ho, A, Besiroglu, T, Hobbhahn, M and Villalobos, P. 2022. ‘Compute Trends Across Three Eras of Machine Learning’. In: 2022 International Joint Conference on Neural Networks (IJCNN). July 2022. pp. 1–8. DOI: 10.1109/IJCNN55064.2022.9891914
    Back to article
  244. 244Shehab, M, Abualigah, L, Shambour, Q, Abu-Hashem, MA, Shambour, MKY, Alsalibi, AI and Gandomi, AH. 2022. ‘Machine learning in medical applications: A review of state-of-the-art methods’. Computers in Biology and Medicine, 145: 105458. DOI: 10.1016/j.compbiomed.2022.105458
    Back to article
  245. 245Silburt, A, Ali-Dib, M, Zhu, C, Jackson, A, Valencia, D, Kissin, Y, Tamayo, D and Menou, K. 2019. ‘Lunar crater identification via deep learning’. Icarus, 317: 27–38. DOI: 10.1016/j.icarus.2018.06.022
    Back to article
  246. 246Sillero, N, Arenas-Castro, S, Enriquez-Urzelai, U, Vale, CG, Sousa-Guedes, D, Martínez-Freiría, F, Real, R and Barbosa, AM. 2021. ‘Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling’. Ecological Modelling, 456: 109671. DOI: 10.1016/j.ecolmodel.2021.109671
    Back to article
  247. 247Smith, ME and Peregrine, P. 2011. ‘Approaches to Comparative Analysis in Archaeology’. In: Smith, ME (ed.). The Comparative Archaeology of Complex Societies. Cambridge: Cambridge University Press. pp. 4–20. DOI: 10.1017/CBO9781139022712.004
    Back to article
  248. 248Song, F, Hooper, L and Loke, YK. 2013. ‘Publication bias: what is it? How do we measure it? How do we avoid it?’. Open Access Journal of Clinical Trials, 5: 71–81. DOI: 10.2147/OAJCT.S34419
    Back to article
  249. 249Song, F, Parekh, S, Hooper, L, Loke, YK, Ryder, J, Sutton, AJ, Hing, C, Kwok, CS, Pang, C and Harvey, I. 2010. ‘Dissemination and publication of research findings : an updated review of related biases’. Health Technology Assessment, 14(8): 1–220. DOI: 10.3310/hta14080
    Back to article
  250. 250Sonnenwald, DH. 2007. ‘Scientific collaboration’. Annu. Rev. Inf. Sci. Technol., 41(1): 643–681. DOI: 10.1002/aris.2007.1440410121
    Back to article
  251. 251Soroush, M, Mehrtash, A, Khazraee, E and Ur, J. 2020. ‘Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq’. Remote Sensing, 12(3). DOI: 10.3390/rs12030500
    Back to article
  252. 252Sparks, GM. 2009. ‘Prying Open the File Drawer’. AMA Journal of Ethics, 11(4): 297–300. DOI: 10.1001/virtualmentor.2009.11.4.jdsc1-0904
    Back to article
  253. 253Stamatopoulos, MI and Anagnostopoulos, C-N. 2016. 3D digital reassembling of archaeological ceramic pottery fragments based on their thickness profile. DOI: 10.48550/arXiv.1601.05824
    Back to article
  254. 254Stott, D, Kristiansen, SM and Sindbaek, SM. 2019. ‘Searching for Viking Age Fortresses with Automatic Landscape Classification and Feature Detection’. Remote Sensing, 11(16): 1881. DOI: 10.3390/rs11161881
    Back to article
  255. 255Strupler, N and Wilkinson, TC. 2017. ‘Reproducibility in the Field: Transparency, Version Control and Collaboration on the Project Panormos Survey’. Open Archaeology, 3(1): 279–304. DOI: 10.1515/opar-2017-0019
    Back to article
  256. 256Štular, B, Lozić, E, Belak, M, Rihter, J, Koch, I, Modrijan, Z, Magdič, A, Karl, S, Lehner, M and Gutjahr, C. 2022. ‘Migration of Alpine Slavs and machine learning: Space-time pattern mining of an archaeological data set’. PloS one, 17(9): e0274687. DOI: 10.1371/journal.pone.0274687
    Back to article
  257. 257Sultana, F, Sufian, A and Dutta, P. 2020. ‘Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey’. Knowledge-Based Systems, 201–202: 106062. DOI: 10.1016/j.knosys.2020.106062
    Back to article
  258. 258Sumbul, G, Charfuelan, M, Demir, B and Markl, V. 2019. ‘Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding’. In: IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium. July 2019. Yokohama, Japan: IEEE. pp. 5901–5904. DOI: 10.1109/IGARSS.2019.8900532
    Back to article
  259. 259Tamkin, A, Brundage, M, Clark, J and Ganguli, D. 2021. Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models. DOI: 10.48550/arXiv.2102.02503
    Back to article
  260. 260Tayan, O, Hassan, A, Khankan, K and Askool, S. 2024. ‘Considerations for adapting higher education technology courses for AI large language models: A critical review of the impact of ChatGPT’. Machine Learning with Applications, 15: 100513. DOI: 10.1016/j.mlwa.2023.100513
    Back to article
  261. 261Tennie, C. 2023. ‘The Earliest Tools and Cultures of Hominins’. In: Tehrani, JJ, Kendal, J and Kendal, R (eds.) The Oxford Handbook of Cultural Evolution. Oxford University Press. DOI: 10.1093/oxfordhb/9780198869252.013.33
    Back to article
  262. 262Tenzer, M, Pistilli, G, Bransden, A and Shenfield, A. 2024. ‘Debating AI in Archaeology: applications, implications, and ethical considerations’. Internet Archaeology, (67). DOI: 10.11141/ia.67.8
    Back to article
  263. 263Thai, H-T. 2022. ‘Machine learning for structural engineering: A state-of-the-art review’. Structures, 38: 448–491. DOI: 10.1016/j.istruc.2022.02.003
    Back to article
  264. 264Thapa, A, Horanont, T, Neupane, B and Aryal, J. 2023. ‘Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis’. Remote Sensing, 15(19): 4804. DOI: 10.3390/rs15194804
    Back to article
  265. 265Thomas, DH. 1973. ‘An Empirical Test for Steward’s Model of Great Basin Settlement Patterns’. American Antiquity, 38(2): 155–176. DOI: 10.2307/279362
    Back to article
  266. 266Toler-Franklin, C, Brown, B, Weyrich, T, Funkhouser, T and Rusinkiewicz, S. 2010. ‘Multi-Feature Matching of Fresco Fragments’. ACM transactions on graphics, 29(6). DOI: 10.1145/1866158.1866207
    Back to article
  267. 267Trotter, EFL, Fernandes, ACM, Fibæk, CS and Keßler, C. 2022. ‘Machine learning for automatic detection of historic stone walls using LiDAR data’. International Journal of Remote Sensing, 43(6): 2185–2211. DOI: 10.1080/01431161.2022.2057206
    Back to article
  268. 268University of Tübingen. 2023. Guidelines on generative AI.
    Back to article
  269. 269Ushizima, D, Xu, K and Monteiro, P. 2020. ‘Materials Data Science for Microstructural Characterization of Archaeological Concrete’. Material Research Society Advances, 5(7): 305–318. DOI: 10.1557/adv.2020.131
    Back to article
  270. 270Valavi, R, Guillera-Arroita, G, Lahoz-Monfort, JJ and Elith, J. 2022. ‘Predictive performance of presence-only species distribution models: a benchmark study with reproducible code’. Ecological Monographs, 92(1): e01486. DOI: 10.1002/ecm.1486
    Back to article
  271. 271Varoquaux, G and Cheplygina, V. 2022. ‘Machine learning for medical imaging: methodological failures and recommendations for the future’. npj Digital Medicine, 5(1): 1–8. DOI: 10.1038/s41746-022-00592-y.
    Back to article
  272. 272Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, Kaiser, L and Polosukhin, I. 2017. Attention Is All You Need. arXiv:1706.03762 [cs].
    Back to article
  273. 273Verhagen, P. 2007. Case studies in archaeological predictive modelling. Archaeological studies Leiden University 14. Leiden: Leiden University Press.
    Back to article
  274. 274Vernon, KB, Yaworsky, PM, Spangler, J, Brewer, S and Codding, BF. 2022. ‘Decomposing Habitat Suitability Across the Forager to Farmer Transition’. Environmental Archaeology, 27(4): 420–433. DOI: 10.1080/14614103.2020.1746880
    Back to article
  275. 275Voorrips, A. (ed.) 1990. Mathematics and information science in archaeology: a flexible framework. Studies in modern archaeology vol. 3. Bonn: Holos.
    Back to article
  276. 276Vos, D, Stafford, R, Jenkins, EL and Garrard, A. 2021. ‘A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data’. PLOS One, 16(3): e0248261. DOI: 10.1371/journal.pone.0248261
    Back to article
  277. 277Wang, D, Zhang, J, Du, B, Xia, G-S and Tao, D. 2023. ‘An Empirical Study of Remote Sensing Pretraining’. IEEE Transactions on Geoscience and Remote Sensing, 61: 1–20. DOI: 10.1109/TGRS.2022.3176603
    Back to article
  278. 278Wang, H, Dang, A, Wu, Z and Mac, S. 2024. ‘Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines’. Computers and Education: Artificial Intelligence, 7: 100326. DOI: 10.1016/j.caeai.2024.100326
    Back to article
  279. 279Wang, Z, Zhao, J, Huang, H and Wang, X. 2022. ‘A Review on the Application of Machine Learning Methods in Tropical Cyclone Forecasting’. Frontiers in Earth Science, 10: 902596. DOI: 10.3389/feart.2022.902596
    Back to article
  280. 280Wentz, R. 2002. ‘Visibility of research: FUTON bias’. The Lancet, 360(9341): 1256. DOI: 10.1016/S0140-6736(02)11264-5
    Back to article
  281. 281Wheatley, D. 2004. ‘Making space for an archaeology of place’. Internet Archaeology, (15). DOI: 10.11141/ia.15.10
    Back to article
  282. 282Wilkinson, MD, Dumontier, M, Aalbersberg, IjJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J-W, da Silva Santos, LB, Bourne, PE, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, CT, Finkers, R, Gonzalez-Beltran, A, Gray, AJG, Groth, P, Goble, C, Grethe, JS, Heringa, J, ’t Hoen, PAC, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, ME, Mons, A, Packer, AL, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S-A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, van der Lei, J, van Mulligen, E, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J and Mons, B. 2016. ‘The FAIR Guiding Principles for scientific data management and stewardship’. Scientific Data, 3(1): 160018. DOI: 10.1038/sdata.2016.18
    Back to article
  283. 283Willey, GR. 1953. ‘Prehistoric settlement patterns in the Virú; Valley, Peru’. Bureau of American Ethnology Bulletin, 155.
    Back to article
  284. 284Wunderlich, T, Wilken, D, Majchczack, B, Segschneider, M and Rabbel, W. 2022. ‘Hyperbola Detection with RetinaNet and Comparison of Hyperbola Fitting Methods in GPR Data from an Archaeological Site’. Remote Sensing, 14(15). DOI: 10.3390/rs14153665
    Back to article
  285. 285Wurzer, G, Kowarik, K and Reschreiter, H. (eds.) 2015. Agent-based Modeling and Simulation in Archaeology. Advances in Geographic Information Science. Cham: Springer International Publishing. DOI: 10.1007/978-3-319-00008-4
    Back to article
  286. 286Xia, G-S, Hu, J, Hu, F, Shi, B, Bai, X, Zhong, Y, Zhang, L and Lu, X. 2017. ‘AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification’. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965–3981. DOI: 10.1109/TGRS.2017.2685945
    Back to article
  287. 287Yang, S, Luo, L, Li, Q, Chen, Y, Wu, L and Wang, X. 2022. ‘Auto-identification of linear archaeological traces of the Great Wall in northwest China using improved DeepLabv3+from very high-resolution aerial imagery’. International Journal of Applied Earth Observation and Geoinformation, 113. DOI: 10.1016/j.jag.2022.102995
    Back to article
  288. 288Yaworsky, PM, Hussain, ST and Riede, F. 2024a. ‘The effects of climate and population on human land use patterns in Europe from 22ka to 9ka ago’. Quaternary Science Reviews, 344: 108956. DOI: 10.1016/j.quascirev.2024.108956
    Back to article
  289. 289Yaworsky, PM, Nielsen, ES and Nielsen, TK. 2024b. ‘The Neanderthal niche space of Western Eurasia 145 ka to 30 ka ago’. Scientific Reports, 14(1): 7788. DOI: 10.1038/s41598-024-57490-4
    Back to article
  290. 290Yaworsky, PM, Vernon, KB, Spangler, JD, Brewer, SC and Codding, BF. 2020. ‘Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument’. PLOS One, 15(10): e0239424. DOI: 10.1371/journal.pone.0239424
    Back to article
  291. 291Yoffee, N and Fowles, S. 2010. ‘L’archéologie dans les sciences humaines’. Diogène, 229–230(1–2): 51–77. DOI: 10.3917/dio.229.0051
    Back to article
  292. 292Zhang, H, Xu, T, Li, H, Zhang, S, Wang, X, Huang, X and Metaxas, D. 2016. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. DOI: 10.48550/ARXIV.1612.03242
    Back to article
  293. 293Zheng, L, Lin, R, Wang, X and Chen, W. 2021. ‘The Development and Application of Machine Learning in Atmospheric Environment Studies’. Remote Sensing, 13(23): 4839. DOI: 10.3390/rs13234839
    Back to article
  294. 294Zheng, Z, Ning, K, Wang, Y, Zhang, J, Zheng, D, Ye, M and Chen, J. 2024. A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends. DOI: 10.48550/arXiv.2311.10372
    Back to article
DOI: https://doi.org/10.5334/jcaa.201 | Journal eISSN: 2514-8362
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Language: English
Submitted on: Jan 23, 2025
Accepted on: Oct 14, 2025
Published on: Dec 12, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
archaeological sciences,
computing sciences,
state of art,
artificial intelligence,
machine learning

© 2025 Mathias Bellat, Jordy Didier Orellana Figueroa, Jonathan Scott Reeves, Ruhollah Taghizadeh-Mehrjardi, Claudio Tennie, Thomas Scholten, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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