Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Machine Learning: A Novel Tool for Archaeology

  • Reference work entry
  • First Online:
Handbook of Cultural Heritage Analysis

Abstract

The nature of archaeology is complex, and is not confined to the humanistic world, but can easily spread to scientific one. Nowadays, it is not uncommon to find archaeologists using new technologies in order to gather, analyze, and interpret data and disseminate the results. It is clear that every aspect of the work of the archaeologists is now actively revolutionized by modern technologies. In this framework, the use of machine learning techniques proves to be extremely interesting. New opportunities for the classification of artifacts, the prediction of site location, and the standardization of remains analysis can now be offered to archaeology. Although the introduction of machine learning in archaeology dates back to the 1970s, an extensive use in day-to-day work is still far away. Probably, the greatest obstacle is the need for a trained archaeologist expert in machine learning. While the improvements that machine learning could introduce in archaeology are clear, the role of the archaeologist in machine learning applications is not. This limitation should be removed increasing the knowledge in the archaeological community. This work means to offer an answer to this need providing descriptions of the main algorithms as well as their applications in the archaeological community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 999.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 1,099.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AE:

Auto-encoder

ANN:

Artificial neural network

AUV:

Autonomous underwater vehicle

BMU:

Best matching unit

CART:

Classification and regression trees

CNN:

Convolutional neural network

CVA:

Canonical variate analysis

DT:

Decision tree

DTMs:

Digital terrain models

ED-XRF:

Energy dispersive X-ray fluorescence

FFNN:

Feed-forward neural network

GIS:

Geographic information system

ISODATA:

Iterative self-organizing data analysis techniques

KNN:

k-nearest neighborhood

LIBS:

Laser-induced breakdown spectroscopy

ML:

Machine learning

MLP:

Multilayer perceptron

PCA:

Principal component analysis

RBFN:

Radial basis function network

ROC:

Receiver operating characteristic curve

SD:

Standard deviation)

SOM:

Self-organizing map

SSE:

Sum of squared error

SVM:

Support vector machine

References

  1. https://www.merriam-webster.com/dictionary/archaeology

  2. https://en.oxforddictionaries.com/definition/archaeology

  3. Werner AE (1969) Scientific methods in archaeology. Naturwissenschaften 56(8):385–392

    Article  Google Scholar 

  4. Bayard DT (1969) Science, theory, and reality in the “new archaeology”. Am Antiq 34(4):376–384

    Article  Google Scholar 

  5. Watson PJ, Leblanc SA, Redman CL (1971) Explanation in archaeology: an explicitly scientific approach. Columbia University Press, New York

    Google Scholar 

  6. Paddayya K (1988–89) The role of hypothesis and traditional archaeology. Bull Deccan Coll Res Inst 47/48:239–247

    Google Scholar 

  7. Dark K (1992) The science of archaeology. Philos Now 3:21–22

    Google Scholar 

  8. McGovern PE (1995) Science in archaeology: a review. Am J Archaeol 99(1):79–142

    Article  Google Scholar 

  9. Kristiansen K (2009) The discipline of archaeology. In: Gosden C, Cunliffe B, Joyce RA (eds) The Oxford handbook of archaeology. Oxford University Press, Oxford

    Google Scholar 

  10. Smith ME, Feinman GM, Drennan RD, Earle T, Morris I (2012) Archaeology as a social science. Proc Natl Acad Sci 109(20):7617–7621

    Article  Google Scholar 

  11. VanPool C, VanPool T (1999) The scientific nature of postprocessualism. Am Antiq 64(1):33–53

    Article  Google Scholar 

  12. Beneš J (2013) The scientific approach in current archaeology. Reflections on the 19th annual meeting of the European Association of Archaeologists in Pilsen 2013. Interdiscip Archaeol Nat Sci Archaeol 4(2):129–131

    Google Scholar 

  13. Clack T, Brittain M (2007) Archaeology and the media. Left Coast Press, Walnut Creek

    Google Scholar 

  14. Smith ME (2018) How archaeology is distorted by science magazine and the National Geographic Society, 6 March 2018. http://publishingarchaeology.blogspot.com/2018/03/how-archaeology-is-distorted-by-science.html

  15. Nikolova L (2015) What was published is as important as how it was published. In: Wilson AT, Edwards B (eds) Open source archaeology: ethics and practice. De Gruyter Open, Warsaw/Berlin

    Google Scholar 

  16. Zimmerman LJ, Vitelli KD, Hollowell-Zimmer J (eds) (2003) Ethical issues in archaeology. Altamira Press in cooperation with the Society for American Archaeology, Walnut Creek

    Google Scholar 

  17. Sorin H, Niccolucci F (2010) The impact of shared information technology on archaeological scientific research. In: Proceedings of international conference on current research information systems (CRIS2000) Helsinki, Finland, 25–27 May

    Google Scholar 

  18. Herrmann JT, Glissmann B, Sconzo P, Pfälzner P (2018) Unmanned aerial vehicle (UAV) survey with commercial-grade instruments: a case study from the eastern Ḫabur archaeological survey, Iraq. J Field Archaeol 43(4):269–283

    Article  Google Scholar 

  19. Cock-Starkey C, June 15, 2017. http://mentalfloss.com/article/501495/4-amazing-archaeological-discoveries-spotted-satellite

  20. Campana S (2017) Drones in archaeology. State-of-the-art and future perspectives. Archaeol Prospect 24(4):275–296

    Article  Google Scholar 

  21. Themistocleous K, Agapiou A, King HM, King N, Hadjimitsis DG (2014) More than a flight: the extensive contributions of UAV flights to archaeological research – the case study of curium site in Cyprus. In: Ioannides M, Magnenat-Thalmann N, Fink E, Žarnić R, Yen A-Y, Quak E (eds) Digital heritage. Progress in cultural heritage: documentation, preservation, and protection. Lecture notes in computer science 8740. Springer, Cham

    Google Scholar 

  22. Wiseman JR, El-Baz F (eds) (2007) Remote sensing in archaeology. Springer, New York

    Google Scholar 

  23. Reindel M, Wagner GA (eds) (2009) New technologies for archaeology: multidisciplinary investigations in Palpa and Nasca, Peru. Springer, Berlin/Heidelberg

    Google Scholar 

  24. Parcak SH (2009) Satellite remote sensing for archaeology. Routledge, London

    Book  Google Scholar 

  25. Parcak S (2007) Satellite remote sensing methods for monitoring archaeological tells in the Middle East. J Field Archaeol 32(1):65–81

    Article  Google Scholar 

  26. Cowley DC (2011) Remote sensing for archaeology and heritage management – site discovery, interpretation and registration. In: Cowley DC (ed) Remote sensing for archaeological heritage management. Europae Archaeologia Consilium (EAC), Bruxelles

    Google Scholar 

  27. Luo L, Wang X, Guo H, Lasaponara R, Shi P, Bachagha N, Li L, Yao Y, Masini N, Chen F, Ji W, Cao H, Li C, Hu N (2018) Google, earth as a powerful tool for archaeological and cultural heritage applications: a review. Remote Sens 10(1558):1–33

    Google Scholar 

  28. Casana J, Jakoby Laugier E (2017) Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war. PLoS One 12(11):e0188589(1)–(31)

    Article  Google Scholar 

  29. Satellite-Based Damage Assessment of Cultural Heritage Sites 2015 Summary report of Iraq, Nepal, Syria & Yemen. http://unosat.org/unitar/downloads/chs/FINAL_Syria_WHS.pdf

  30. Casana J, Panahipour M (2014) Satellite-based monitoring of looting and damage to archaeological sites in Syria. J East Medit Archaeol Herit Stud 2(2):128–151

    Google Scholar 

  31. Serafin J, Di Cicco M, Bonanni TM, Grisetti G, Iocchi L, Nardi D (2016) Robots for exploration, digital preservation and visualization of archaeological sites. In: Bordoni L, Mele F, Sorgente A (eds) Artificial intelligence for cultural heritage. Cambridge Scholars Publishing, Newcastle upon Tyne

    Google Scholar 

  32. Sample I (2018) New technologies bring marine archaeology treasures to light, 4 Mar 2018. https://www.theguardian.com/science/2016/dec/29/new-technologies-bring-marine-archaeology-treasures-to-light

  33. Dormehl L (2017) Today’s archaeologists are putting down shovels and turning to tech, 23 Dec 2017. https://www.digitaltrends.com/cool-tech/tech-changing-archaeology/

  34. Hawass Z (2015) Magic of the pyramids: my adventures in archaeology. Harmakis Edizioni, Montevarchi

    Google Scholar 

  35. Ventura A (2013) Robot detecta tres camerás en subsuelo de Teotihuacan, 22 Apr 2013. http://archivo.eluniversal.com.mx/notas/918346.html

  36. Bauval R Secret Chamber Revisited: The Quest for the Lost Knowledge of Ancient Egypt, Di Robert Bauval

    Google Scholar 

  37. https://www.smithsonianmag.com/history/discovery-secret-tunnel-mexico-solve-mysteries-teotihuacan-180959070/. A secret tunnel found in Mexico May finally solve the mysteries of Teotihuacán

  38. Allotta B, Costanzi R, Ridolfi A, Salvetti O, Reggiannini M, Kruusmaa M, Salumae T, Lane DM, Frost G, Tsiogkas N (2018) The ARROWS Project: robotic technologies for underwater archaeology. IOP Conf Ser: Mater Sci Eng 364:012088(1)–(8)

    Article  Google Scholar 

  39. Bingham B, Foley BJ, Singh H, Camilli R, Delaporta K, Eustice R, Mallios A, Mindell D, Roman C, Sakellario D (2010) Robotic tools for deep water archaeology: surveying an ancient shipwreck with an autonomous underwater vehicle. J Field Robot 27(6):702–717

    Article  Google Scholar 

  40. Grisetti G (2014) Robots for the digitization of hard-to-access cultural heritage sites. In: Proceedings of 2nd international conference RICH 2014 – robotics: innovation for cultural heritage

    Google Scholar 

  41. Puhar EG, Erič M, Kavkler K, Cramer A, Celec K, Korat L, Jaklič A, Solina F (2018) Comparison and deformation analysis of five 3D models of the Paleolithic wooden point from the Ljubljanica river. In: 2018 IEEE international conference on metrology for archaeology and cultural heritage, 22–24 October 2018, Cassino, Italy. IEEE

    Google Scholar 

  42. Landeschi G (2018) Rethinking GIS, three-dimensionality and space perception in archaeology. World Archaeol 51:17–32

    Article  Google Scholar 

  43. Galeazzi F (2016) Towards the definition of best 3D practices in archaeology: assessing 3D documentation techniques for intra-site data recording. J Cult Herit 17:159–169

    Article  Google Scholar 

  44. Dawn S, Biswas P (2019) Technologies and methods for 3D reconstruction. In: Thampi SM, Marques O, Krishnan S, Li K-C, Ciuonzo D, Kolekar MH (eds) Advances in signal processing and intelligent recognition systems. Springer Nature, Singapore

    Google Scholar 

  45. Doulamis A (2018) Automatic 3D reconstruction from unstructured videos combining video summarization and structure from motion. Front ICT 5(29):1–13

    Google Scholar 

  46. McCarthy JK, Benjamin J, Winton T, van Duivenvoorde W (eds) (2019) 3D recording and interpretation for maritime archaeology. Springer International Publishing

    Google Scholar 

  47. Stylianidis E (2016) In: Remondino F (ed) 3D recording, documentation and management of cultural heritage. Whittles Publishing

    Google Scholar 

  48. Remondino F, Campana S (eds) (2014) 3D recording and modelling in archaeology and cultural heritage: theory and best practices. BAR international series 2598. BAR Publishing, Oxford

    Google Scholar 

  49. Forte M, Dell’Unto N, Issavi J, Onsurez L, Lercari N (2012) 3D Archaeology at Çatalhöyük. Int J Herit Digit Era 1(3):351–378

    Article  Google Scholar 

  50. Deravignone L, Macchi Jánica G (2006) Artificial neural networks in archaeology. Archeol Calcolatori 17:121–136

    Google Scholar 

  51. van den Dries MH (1998) Archaeology and the application of artificial intelligence: case studies on use-wear analysis of prehistoric flint tools. Faculty of Archaeology, University of Leiden

    Google Scholar 

  52. Puyol-Gruiart J (1998) Computer applications and quantitative methods in archaeology. In: Proceedings of the 26th conference, Barcelona, 19–27

    Google Scholar 

  53. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210–229

    Article  Google Scholar 

  54. Mitchell TM (1997) Machine learning. McGraw-Hill Science

    Google Scholar 

  55. Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, New York

    Book  Google Scholar 

  56. Oren Steinberg, June 6, 2017: Machine learning – art or science? http://insurancethoughtleadership.com/machine-learning-art-or-science/

  57. Hall P (2018) On the art and science of machine learning explanations. In: Proceedings of 2018 Joint Statistical Meetings (JSM) arXiv:1810.02909 [stat.ML]

    Google Scholar 

  58. Carbonell JG, Michalski RS, Mitchell TM (1983) Machine learning: A historical and methodological analysis. AI Mag 4(3):69–79

    Google Scholar 

  59. Carbonell JG, Michalski RS, Mitchell TM (1983) An overview of machine learning. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning. Springer, Berlin/Heidelberg

    Google Scholar 

  60. Kubat M (2015) An introduction to machine learning. Springer International Publishing

    Book  Google Scholar 

  61. Kwangjo K, Aminanto ME, Tanuwidjaja HC (2018) Network intrusion detection using deep learning. A feature learning approach. Springer Singapore

    Google Scholar 

  62. Aggarwal CC (2018) Machine learning for text. Springer International Publishing

    Book  Google Scholar 

  63. Kwok JT, Zhou Z-H, Xu L (2015) Machine learning. In: Kacprzy KJ, Pedrycz W (eds) Springer handbook of computational intelligence. Springer, Berlin/Heidelberg

    Google Scholar 

  64. Sutton RS, Barto AG (1998) In: Book B (ed) Reinforcement learning: an introduction. The MIT Press, Cambridge

    Google Scholar 

  65. Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Ann Data Sci 2(2):165–193

    Article  Google Scholar 

  66. Reilly P, Rahtz S (eds) (1992) Archaeology and the information age: a global perspective. Routledge, London/New York

    Google Scholar 

  67. Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory IT-28(2):129–137

    Article  Google Scholar 

  68. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57

    Article  Google Scholar 

  69. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic, Norwell

    Book  Google Scholar 

  70. Ball GH, Hall DJ (1965) Isodata: a method of data analysis and pattern classification. Stanford Research Institute, Menlo Park

    Google Scholar 

  71. Bartenhagen C, Klein HU, Ruckert C, Jian X, Dugas M (2010) Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data. BMC Bioinformatics 11(567):1–11

    Google Scholar 

  72. Arul Kumar V, Elavarasan N (2014) A survey on dimensionality reduction technique. Int J Emerg Trends Technol Comput Sci (IJETTCS) 3(6):36–41

    Google Scholar 

  73. Bellman RE (1962) Dynamic programming. Rand Corporation, Santa Monica

    Book  Google Scholar 

  74. Dash M, Liu H, Yao J (1997) Dimensionality reduction of unsupervised data. In: Proceedings ninth IEEE international conference on tools with artificial intelligence, pp 532–539

    Google Scholar 

  75. Hinton G, Sejnowski TJ (eds) (1999) Unsupervised learning: foundations of neural computation. The MIT Press, Cambridge

    Google Scholar 

  76. Cios KJ, Pedrycz W, Swiniarski RW, Kurgan L (2007) Data mining. a knowledge discovery approach. Springer

    Google Scholar 

  77. Cover T, Hart P (1967) Nearest neighbor pattern classification. Trans Inf Theory 13(1):21–27

    Article  Google Scholar 

  78. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York

    Google Scholar 

  79. Hart P (1968) The condensed nearest neighbor rule. Trans Inf Theory 14(3):515–516

    Article  Google Scholar 

  80. Cover TM (1968) Estimates by the nearest neighbor rule. Trans Inf Theory 14(1):50–55

    Article  Google Scholar 

  81. Hellman ME (1970) The nearest neighbor classification rule with a reject option. Trans Syst Sci Cybern 6(3):179–185

    Article  Google Scholar 

  82. Tomek I (1976) A generalization of the k-NN rule. Trans Syst Cybern SMC-6(2):121–126

    Article  Google Scholar 

  83. Dasarathy BV, Belur SV (1977) Visiting nearest neighbor-a survey of nearest neighbor classification techniques. In: Proceedings of international conference on cybernetics and society, pp 630–636

    Google Scholar 

  84. Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: Meersman R, Tari Z, Schmidt DC (eds) On the move to meaningful Internet systems. Springer, Berlin/Heidelberg

    Google Scholar 

  85. Kataria A, Singh MD (2013) A review of data classification using K-nearest neighbour algorithm. Int J Emerg Technol Adv Eng 3(6):354–360

    Google Scholar 

  86. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    Google Scholar 

  87. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    Google Scholar 

  88. Lowd D, Domingos P (2005) Naive Bayes models for probability estimation. In: Proceedings of the 22th international conference on machine learning, pp 529–536

    Google Scholar 

  89. Park D-C (2016) Image classification using naïve Bayes classifier. Int J Comp Sci Electron Eng 4(3):135–139

    Google Scholar 

  90. Hunt E (1962) Concept learning. Wiley, New York

    Google Scholar 

  91. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont

    Google Scholar 

  92. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Article  Google Scholar 

  93. Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20(3):73–297

    Article  Google Scholar 

  94. Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  95. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

    Book  Google Scholar 

  96. Mc Culloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  Google Scholar 

  97. Rojas R (1996) Neural Networks. A systematic introduction. Springer, Berlin

    Google Scholar 

  98. Bengio Y (2009) Learning deep architectures for AI. now Publishers Inc, Hanover

    Book  Google Scholar 

  99. The Neural Network Zoo: September 14, 2016 by Fjodor van Veen: https://www.asimovinstitute.org/author/fjodorvanveen/

  100. Cao W, Wang X, Ming Z, Gao J (2018) A review on Neural Networks with random weights. Neurocomputing 275:278–287

    Article  Google Scholar 

  101. Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinformatics 19(6):1236–1246

    Article  Google Scholar 

  102. Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352–2449

    Article  Google Scholar 

  103. McCann MT, Jin KH, Unser M (2017) Convolutional Neural Networks for inverse problems in imaging: a review. IEEE Signal Process Mag 34(6):85–95

    Article  Google Scholar 

  104. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning, arXiv preprint arXiv:1506.00019

    Google Scholar 

  105. Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  Google Scholar 

  106. Schmidhuber J (2015) Deep learning in Neural Networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  107. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386

    Article  Google Scholar 

  108. Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward Neural Networks. Chemom Intell Lab Syst 39(1):43–62

    Article  Google Scholar 

  109. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  Google Scholar 

  110. LeCun Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  111. Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks (Technical report). RSRE memorandum, vol 414. Royals Signals & Radar Establishment, pp 1–34

    Google Scholar 

  112. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2(3):321–355

    Google Scholar 

  113. Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4–5):439–458

    Article  Google Scholar 

  114. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69

    Article  Google Scholar 

  115. Kohonen T (1995) Self-organizing maps. Springer, Heidelberg

    Book  Google Scholar 

  116. Ballard DH (1987) Modular learning in Neural Networks. In: Proceedings of the 6th national conference on artificial intelligence 1, pp 279–284

    Google Scholar 

  117. Frost G, Maurelli F, Lane DM (2015) Reinforcement learning in a behaviour-based control architecture for marine archaeology. In: Oceans 2015. IEEE, pp 1–5

    Google Scholar 

  118. Hodson FR (1970) Cluster analysis and archaeology: some new developments and applications. World Archaeol 1(3):299–320

    Article  Google Scholar 

  119. Kowalski BR (1972) Classification of archaeological artifacts by applying pattern recognition to trace element data. Anal Chem 44(13):2176–2180

    Article  Google Scholar 

  120. Mircea IG, Limboi SG, Petruşel MR (2015) A new unsupervised learning based approach for gender detection of human archaeological remains. Studia Univ Babeş-Bolyai Inform 60(2):5–20

    Google Scholar 

  121. Miholca DL, Czibula G, Mircea IG, Czibula IG (2016) Machine learning based approaches for sex identification in bioarchaeology. In: Proceedings of IEEE 18th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC), pp 311–314

    Google Scholar 

  122. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Proceedings of of 14th international conference on machine learning, pp 412–442

    Google Scholar 

  123. Wei HL, Billings S (2007) Feature subset selection and ranking for data dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):162–166

    Article  Google Scholar 

  124. Singh DAG, Balamurugan SAA, Leavline EJ (2015) An unsupervised feature selection algorithm with feature ranking for maximizing performance of the classifiers. Int J Automat Comput 12(5):511–517

    Article  Google Scholar 

  125. Dalton L, Ballarin V, Brun M (2009) Clustering algorithms: on learning, validation, performance, and applications to genomics. Curr Genomics 10(6):430–445

    Article  Google Scholar 

  126. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis, Wiley series in probability and mathematical statistics. Wiley, Hoboken

    Book  Google Scholar 

  127. Novaković JD, Veljović A, Ilić SS, Papić Z, Tomović M (2017) Evaluation of classification models in machine learning. Theory Appl Math Comput Sci 7(1):39–46

    Google Scholar 

  128. Rogers S, Girolami M (2012) A first course in machine learning, machine learning & pattern recognition. CRC Press, Cambridge, UK

    Google Scholar 

  129. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437

    Article  Google Scholar 

  130. Christmas J, Pitts M (2018) Classifying and visualising Roman pottery using computer-scanned typologies. Int Archaeol 50

    Google Scholar 

  131. Wang L, Zhao C (2016) Classification technique for HIS. In: Hyperspectral image processing. Springer

    Chapter  Google Scholar 

  132. Ting KM (2017) Confusion matrix. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer, Boston

    Google Scholar 

  133. Wilczek J, Monna F, Gabillot M, Navarro N, Ruscha L, Chateau C (2015) Unsupervised model-based clustering for typological classification of Middle Bronze Age flanged axes. J Archaeol Sci Rep 3:381–391

    Google Scholar 

  134. McLachlan G, Peel D (2000) Finite mixture models. Wiley-Interscience, New York

    Book  Google Scholar 

  135. Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41(8):578–588

    Article  Google Scholar 

  136. Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49(3):803–821

    Article  Google Scholar 

  137. Gansell AR, Van de Meent JW, Zairis S, Wiggins CH (2015) Stylistic clusters and the Syrian/South Syrian tradition of first millennium BCE Levantine ivory carving: a machine learning approach. J Archaeol Sci 44:194–205

    Article  Google Scholar 

  138. Keeney J, Hickey R (2015) Using satellite image analysis for locating prehistoric archaeological sites in Alaska’s Central Brooks Range. J Archaeol Sci Rep 3:80–89

    Google Scholar 

  139. Verhagen P, Drgu L (2013) Discovering the Dutch mountains. An experiment with automated landform classification for purposes of archaeological predictive mapping. In: Proceedings of 38th annual conference on computer applications and quantitative methods in archaeology, pp 213–216

    Google Scholar 

  140. Masini N (2018) Medieval archaeology under the canopy with LiDAR. The (Re)discovery of a medieval fortified settlement in Southern Italy. Remote Sens 10(10):1–26

    Article  Google Scholar 

  141. Ciminale M, Gallo D, Lasaponara R, Masini N (2009) A multiscale approach for reconstructing archaeological landscapes: applications in northern Apulia (Italy). Archaeol Prospect 16(3):143–153

    Article  Google Scholar 

  142. Lasaponara R, Masini N (2018) Space-based identification of archaeological illegal excavations and a new automatic method for looting feature extraction in desert areas. Surv Geophys 39(6):1323–1346

    Article  Google Scholar 

  143. Cacciari I, Pocobelli G, Cicola S, Siano S (2018) Discrimination of soil texture and contour recognitions during archaeological excavation using Machine Learning. IOP Conf Ser Mater Sci Eng 364:012042(1)–(8)

    Article  Google Scholar 

  144. Cacciari I, Pocobelli G, Siano S (2017) Machine learning: a toolkit for speeding up archaeological stratigraphic identification. In: Proceedings of IMEKO international conference on metrology for archaeology and cultural heritage. MetroArchaeo

    Google Scholar 

  145. Crescioli M, D’Andrea A, Niccolucci F (2010) A GIS-based analysis of the Etruscan cemetery of Pontecagnano using fuzzy logic. In: Lock G (ed) Beyond the map: archaeology and spatial technologies. IOS Press, Amsterdam

    Google Scholar 

  146. Hermon S, Niccolucci F, Alhaique F, Iovino MR, Leonini V (2003) Archaeological typologies, an archaeological fuzzy reality. In: Proceedings of CAA 2003, computer applications and quantitative methods in archaeology, pp 30–33

    Google Scholar 

  147. Baxter MJ (2009) Archaeological data analysis and fuzzy clustering. Archaeometry 51(6):1035–1054

    Article  Google Scholar 

  148. Klinger R, Schwanghart W, Schütt B (2011) Landscape classification using principal component analysis and fuzzy classification: archaeological sites and their natural surroundings in the Central Mongolia. Erde 142(3):213–233

    Google Scholar 

  149. Finke PA, Meylemans E, Van de Wauw J (2008) Mapping the possible occurrence of archaeological sites by Bayesian inference. J Archaeol Sci 35(10):2786–2796

    Article  Google Scholar 

  150. Li-Ying Q, Ke-Gang W (2010) Kernel fuzzy clustering based classification of ancient-ceramic fragments. In: Proceedings of 2nd IEEE international conference on information management and engineering, pp 348–350

    Google Scholar 

  151. Wang S, Hu Q, Wang F, Ai M, Zhong R (2017) A microtopographic feature analysis-based LiDAR data processing approach for the identification of Chu Tombs. Remote Sens 9(9):880(1)–(17)

    Article  Google Scholar 

  152. Malinverni ES, Fangi G (2009) Comparative cluster analysis to localize emergencies in archaeology. J Cult Herit 10S:e10–e19

    Article  Google Scholar 

  153. Conolly J, Lake M (2006) Geographical information systems in archaeology. Cambridge University Press, Cambridge

    Book  Google Scholar 

  154. Bietti A, Burani A, Zampetti D (1992) An example of supervised classification in paleolithic archaeology. Archeol Calcolatori 3:7–17

    Google Scholar 

  155. Makridis M, Daras P (2012) Automatic classification of archaeological pottery sherds. J Comput Cult Herit 5(4):1–21

    Article  Google Scholar 

  156. Reddi SS, Rudin SF, Keshavan HR (1984) An optical multiple threshold scheme for image segmentation. IEEE Trans Syst Man Cybern 14(4):661–665

    Article  Google Scholar 

  157. Smith P Bespalov D, Shokoufandeh A, Jeppson P (2010) Classification of archaeological ceramic fragments using texture and color descriptors. In: Proceedings of 2010 IEEE computer society conference on computer vision and pattern recognition – workshops, pp 49–84

    Google Scholar 

  158. Hristov V, Agre G (2013) A software system for classification of archaeological artefacts represented by 2D plans. Cybern Inf Technol 13(2):82–96

    Google Scholar 

  159. van der Maaten LJP, Boon PJ (2006) Coin-o-matic: a fast and reliable system for coin classification. In: Proceedings of the MUSCLE coin workshop. Vienna University, Berlin, pp 7–17

    Google Scholar 

  160. van der Maaten L, Boon P, Lange G (2006) Computer vision and machine learning for archaeology. In: Clark JT, Hagemeister EM (eds) Digital discovery. exploring new frontiers in human heritage. CAA2006. Computer applications and quantitative methods in archaeology. Proceedings of the 34th conference, Fargo, United States, April 2006. Archaeolingua, Budapest, pp 476–482. CD-ROM

    Google Scholar 

  161. van der Maaten LJP, Postma EO (2006) Towards automatic coin classification. In: Proceedings of the EVA-Vienna, pp 19–26

    Google Scholar 

  162. Litton CD, Buck CE (1995) Review article the Bayesian approach to the interpretation of archaeological data. Archaeometry 37(1):1–24

    Article  Google Scholar 

  163. Buck CE, Cavanagh WG, Litton CD (1996) Bayesian approach to interpreting archaeological data. Wiley, Chichester

    Google Scholar 

  164. Robertson IG (1999) Spatial and multivariate analysis, random sampling error, and analytical noise: empirical Bayesian methods at Teotihuacan, Mexico. Am Antiq 64(1):137–152

    Article  Google Scholar 

  165. Millard A (2005) What can Bayesian statistics do for archaeological predictive modelling? In: Van Leusen M, Kamermans H (eds) Predictive modelling for archaeological heritage management: a research agenda, vol 29. Nederlandse Archeologische Rapporten, pp 169–182

    Google Scholar 

  166. Finke PA, Meylemans E, Van de Wauw J (2008) Mapping the possible occurrence of archaeological sites by Bayesian inference. J Archaeol Sci 35:2788–2796

    Article  Google Scholar 

  167. Rivals F, Prignano L, Semprebon GM, Lozano S (2015) A tool for determining duration of mortality events in archaeological assemblages using extant ungulate microwear. Sci Rep 5(17330):1–12

    Google Scholar 

  168. McLeod N (2018) The quantitative assessment of archaeological artifact groups: beyond geometric morphometrics. Quat Sci Rev 201:319–348

    Article  Google Scholar 

  169. Campbell NA, Atchley WR (1981) The geometry of canonical variate analysis. Syst Zool 30(3):268–280

    Article  Google Scholar 

  170. Espa G, Benedetti R, De Meo A, Ricci U, Espa S (2006) GIS based models and estimation methods for the probability of arcaheological site location. J Cult Herit 7:147–155

    Article  Google Scholar 

  171. Breinman L, Freidman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont

    Google Scholar 

  172. Hörr C, Lindinger E, Brunnett G (2008) New paradigms, for automated classification of pottery. In: Proceedings of 36th CAA, pp 268–277

    Google Scholar 

  173. Charalambous E, Dikomitou-Eliadou M, Milis GM, Mitsis G, Eliades DG (2016) An experimental design for the classification of archaeological ceramic data from Cyprus, and the tracing of inter-class relationships. J Archaeol Sci Rep 7:465–471

    Google Scholar 

  174. Arriaza MC, Domínguez-Rodrigo M (2016) When felids and hominins ruled at Olduvai Gorge: A machine learning analysis of the skeletal profiles of the non-anthropogenic Bed I sites. Quat Sci Rev 139:43–52

    Article  Google Scholar 

  175. Huber HR, Jorgensen JC, Butler VL, Baker G, Stevens R (2011) Can salmonids (Oncorhynchus spp.) be identified to species using vertebral morphometrics? J Archaeol Sci 38:136–146

    Article  Google Scholar 

  176. Hefner JT, Spradley MK, Anderson B (2014) Ancestry assessment using random forest modeling. J Forensic Sci 59:583–589

    Article  Google Scholar 

  177. Menze BH, Ur JA (2014) Multitemporal fusion for the detection of static spatial patterns in multispectral satellite images – with application to archaeological survey. IEEE J Sel Top Appl Earth Obs Remote Sens 7(8):3513–3524

    Article  Google Scholar 

  178. Navega D, Coelho C, Vicente R, Ferreira MT, Wasterlain S, Cunha E (2015) AncesTrees: ancestry estimation with randomized decision trees. Int J Legal Med 129:1145–1153

    Article  Google Scholar 

  179. Grilli E, Dininno D, Petrucci G, Remondino F (2018) From 2D to 3D supervised segmentation and classification for cultural heritage applications. Proc Int Arch Photogram Remote Sens Spat Inf Sci XLII-2:399–406

    Article  Google Scholar 

  180. Tolpygo A (20 Jan 2017) Archaeological site prediction using machine learning. https://sflscientific.com/data-science-blog/2017/1/9/archaeological-site-prediction-using-machine-learning

  181. Qi J, Zhang T, Tang H, Li H (2018) Rapid classification of archaeological ceramics via laser-induced breakdown spectroscopy coupled with random forest. Spectrochim Acta B 149:288–293

    Article  Google Scholar 

  182. Guyot A, Hubert-Moy L, Lorho T (2018) Detecting neolithic burial mounds from LiDAR-derived elevation data using a multi-scale approach and machine learning techniques. Remote Sens 10(2):1–19

    Article  Google Scholar 

  183. Brunner G, Burkhardt H (2008) Classification and retrieval of ancient watermarks. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Springer, Berlin/Heidelberg

    Google Scholar 

  184. Debroutelle T, Janvier R, Chetouani A, Treuillet S, Exbrayat M, Martin L, Jesset S (2015) Automatic pattern recognition on archaeological ceramic by 2D and 3D image analysis: a feasibility study. In: Proceedings of international conference on image processing theory, tools and applications (IPTA), pp 224–228

    Google Scholar 

  185. Gansell AR, Tamaru IK, Jakulin A, Wiggins CH (2007) Predicting regional classification of Levantine ivory sculptures: a machine learning approach. In: Proceedings of 34th computer applications & quantitative methods in archaeology conference. Archaeolingua, pp 483–492

    Google Scholar 

  186. Soumya A, Hemantha Kumar G (2011) Svm classifier for the prediction of era of an epigraphical script. Int J Peer Peer Netw (IJP2P) 2(2):122

    Google Scholar 

  187. Ionescu VS, Czibula G, Teletin M (2018) Supervised Learning techniques for body mass estimation in bioarchaeology. In: Balas V, Jain L, Balas M (eds) Soft computing applications. SOFA 2016. Advances in intelligent systems and computing, vol 634. Springer, Cham

    Google Scholar 

  188. Ionescu VS (2015) Applying support vector regression methods for height estimation in archaeology. Univ Babes-Bolyai Inform 60(2):70–82

    Google Scholar 

  189. Mehrer MW, Wescot KL (2006) GIS and archaeological site location modeling. CRC, Boca Raton

    Google Scholar 

  190. Zhu X, Chen F, Guo H (2018) A spatial pattern analysis of frontier passes in China’s Northern Silk Road Region using a scale optimization BLR archaeological predictive model. Heritage 1(1):15–32

    Article  Google Scholar 

  191. Carleton WC, Cheong KF, Savage D, Barry J, Conolly J, Iannone G (2017) A comprehensive test of the Locally-Adaptive Model of Archaeological Potential (LAMAP). J Archaeol Sci Rep 11:59–68

    Google Scholar 

  192. Klassen S, Weed J, 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(1)–(17)

    Article  Google Scholar 

  193. Gibson PM (1993) The potentials of hybrid Neural Network models for archaeofaunal ageing and interpretation. In: Andresen J, Madsen T, Scollar I (eds) Computing the past. Computer applications and quantitative methods in archaeology. CAA92. Aarhus University Press, Aarhus, pp 263–272

    Google Scholar 

  194. Gibson PM (1993) The application of hybrid Neural Network models to estimate age of domestic ungulates. Int J Osteoarchaeol 3(1):45–48

    Article  Google Scholar 

  195. Reeler C (1997) Neural networks and fuzzy logic analysis in archaeology. In: Dingwall L, Exon S, Gaffney V, Laflin S, Van Leusen M (eds) Archaeology in the age of internet. Archaeo Press, Oxford

    Google Scholar 

  196. Ramil A, Lόpez AJ, Yáñez A (2008) Application of artificial Neural Networks for the rapid classification of archaeological ceramics by means of laser induced breakdown spectroscopy (LIBS). Appl Phys A Mater Sci Process 92(1):197–2002

    Article  Google Scholar 

  197. Barcelo JA, Pijoan Lopez J (2004) Cutting or scrapping? Using neural networks to distinguish kinematics in use wear analysis. In: der Stadt Wien M (ed) Enter the past. The e-way into the four dimensions of culture heritage. BAR Int. Series 1227. Archaeo Press, Oxford, pp 427–431

    Google Scholar 

  198. Kazimi B, Thiemann F, Malek K, Sester M, Khoshelham K (2018) Deep learning for archaeological object detection in airborne laser scanning data. In: Proceedings of 2nd workshop on computing techniques for spatio-temporal data in archaeology and cultural heritage, pp 21–35

    Google Scholar 

  199. Tanevska V, Kuzmanovski I, Grupče O (2007) Provenance determination of Vinica Terra Cotta icons using self-organising maps. Ann Chim 97(7):541–552

    Article  Google Scholar 

  200. Toyota RG, Munita CS, Boscarioli C, Hernandez EDM, Neves EG, Demartini CC (2009) Neural network applied to elemental archaeological Marajoara ceramic compositions. In: Proceedings of international nuclear Atlantic conference (INAC 2009), vol 41, no 36, pp 1–9

    Google Scholar 

  201. Lopez-Molinero A, Castro A, Pino J, Perez-Arantegui J, Castillo JR (2000) Classification of ancient Roman glazed ceramics using the neural network of self-organizing maps. Fresenius J Anal Chem 367(6):586–589

    Article  Google Scholar 

  202. Fermo P, Cariati F, Ballabio D, Consonni V, Bagnasco Gianni G (2004) Classification of ancient Etruscan ceramics using statistical multivariate analysis of data. Appl Phys A Mater Sci Process 79:299–307

    Article  Google Scholar 

  203. Lu P, Tian Y, Yang R (2013) The study of size-grade of prehistoric settlements in the Circum-Songshan area based on SOFM network. J Geogr Sci 23(3):538–548

    Article  Google Scholar 

  204. Vafidis A, Economou N, Spanoudakis NS, Hamdan HA, Niniou-Kindeli V (2007) Application of classification methods on geophysical data from the archaeological site of Aptera, Chania, Greece. In: Proceedings of 13th EAGE European meeting of environmental and engineering geophysics

    Google Scholar 

  205. Deufemia V, Paolino L (2013) Combining unsupervised clustering with a non-linear deformation model efficient petroglyph recognition. In: Bebis G et al (eds) Advances in visual computing. ISVC 2013. Lecture notes in computer science, vol 8034. Springer, Berlin/Heidelberg

    Google Scholar 

  206. Keysers D, Gollan C, Ney H (2004) Local context in non-linear deformation models for handwritten character recognition. In: Proceedings of international conference on pattern recognition, ICPR 2004. IEEE, pp 511–514

    Google Scholar 

  207. Deselaers T, Weyand T, Keysers D, Macherey W, Ney H (2006) Fire in image CLEF 2005: combining content-based image retrieval with textual information retrieval. In: Peters C, Gey FC, Gonzalo J, Müller H, Jones GJF, Kluck M, Magnini B, de Rijke M, Giampiccolo D (eds) CLEF 2005. LNCS, vol 4022. Springer, Heidelberg, pp 652–661

    Google Scholar 

  208. Can G, Odobez JM, Gatica-Perez D (2016) Evaluating shape representations for Maya glyph classification. J Comput Cult Herit 9(3):1–26

    Article  Google Scholar 

  209. Roman-Rangel E, Can G, Marchand-Maillet S, Hu R, Pallán Gayol C, Krempel G, Spotak J, Odobez JM, Gatica-Perez D (2016) Transferring neural representations for low-dimensional indexing of Maya Hieroglyphic art. In: Hua G, Jégou H (eds) Computer vision – ECCV 2016 workshops. ECCV 2016. Lecture notes in computer science, vol 9913. Springer, Cham, pp 842–855

    Google Scholar 

  210. Cavalli RM, Licciardi GA, Chanussot J (2013) Detection of anomalies produced by buried archaeological structures using nonlinear principal component analysis applied to airborne hyperspectral image. IEEE J Sel Top Appl Earth Obs Remote Sens 6(2):659–669

    Article  Google Scholar 

  211. Sharafi S, Fouladvand S, Simpson I, Barcelo Alvarez JA (2016) Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran. J Archaeol Sci Rep 8:206–215

    Google Scholar 

  212. Skarlatos D (2017) 2nd iMARECULTURE Newsletter, 2(1):1–6. https://imareculture.eu/2nd-imareculture-newsletter/

  213. Li P, Sun M, Wang Z, Chai B (2018) OPTICS-based unsupervised method for flaking degree evaluation on the Murals in Mogao Grottoes. Sci Rep 8(1):15954(1)–(10)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Istituto di Fisica Applicata “Nello Carrara”, Consiglio Nazionale delle Ricerche, Sesto Fiorentino, Italy

    I. Cacciari

  2. Istituto di Scienze del Patrimonio Culturale, Consiglio Nazionale delle Ricerche, Florence, Italy

    G. F. Pocobelli

Authors
  1. I. Cacciari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. F. Pocobelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Cacciari .

Editor information

Editors and Affiliations

  1. Department of Geosciences, University of Malta, Msida, Malta

    Sebastiano D'Amico

  2. University of Messina, Messina, Italy

    Valentina Venuti

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Cacciari, I., Pocobelli, G.F. (2022). Machine Learning: A Novel Tool for Archaeology. In: D'Amico, S., Venuti, V. (eds) Handbook of Cultural Heritage Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-60016-7_33

Download citation

Publish with us

Policies and ethics

Search

Navigation