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Data Driven Archaeology. Gabriele Gattiglia, Università di Pisa

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Data Driven Humanities. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

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Data Driven Archaeology. Gabriele Gattiglia, Università di Pisa

  1. 1. https://www.flickr.com/photos/wwworks/ Data Driven Archaeology Gabriele Gattiglia Università di Pisa
  2. 2. i s a research Lab of the University of Pisa, in which archaeologists, mathematicians and geologists deal with: • mathematical models for archaeology (www.mappaproject.org) • open data (www.mappaproject.org/mod) and now they are starting to explore risks and potentiality of a Big Data approach to archaeology. MAPPA Lab 
  3. 3. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics [Datafication]
  4. 4. From digitisation….  Digitisation has changed archaeology deeply. It has boosted the volume of data that can be  analysed.  ….to datafication • to transform objects, processes, etc. in a quantified format so they can be  tabulated and  analysed.  • to record new information; • to produce a constant flow of data, starting from the data produced by archaeological  practice, such as locations and relationships between finds and sites, that the archaeological  community should have available with minimum time delay, to process again and again.  • to fit Big Data approach and permit mathematical analyses to identify non‐linear  relationships  These new data can modify the way we conduct our analyses, increase our capacity to process  and visualise information in novel ways, and more decisively, provide new ways of doing  archaeological research.  “Archaeology is a place within the social sciences and Humanities where the nature  of the work deals with Big Data” As archaeologists, we are well‐aware that it is easier to create new datasets than transform old  ones. 
  5. 5. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics *Data recording
  6. 6. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics *access to data
  7. 7. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics *Data Management
  8. 8. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics *Data Analysis
  9. 9. https://www.flickr.com/photos/wwworks/ [Distribution Map] [Data Visualisation]
  10. 10. Predictive Map of Archaeological Potential Archaeological Map Paleogeographical Map Mathematical model Open digital archaeological archive Open Data Cooperation Transparency Geology Archaeology Mathematics Pisa, Italy
  11. 11. DATA MODEL • Urban data • Historical cartography data • Geographical/ geomorphological data • Archaeological data PRIMARY DATA • available data SECONDARY DATA
  12. 12. MAPPAGIS www.mappaproject.org/webgis
  13. 13. 7. Report, explain, predict 1. Real-world problem 3. Formulate the abstract problem 4. Solve the abstract problem 5. Interpret the solution 6. Verify the model TESTING 2. Make assumptions BASIC RULES governing the system PROBABILITY DISTRIBUTIONS MATHEMATICAL MODELS STATISTICAL MODELS
  14. 14. A page pointing to other pages distributes its importance to those pages         13 312 21 3/1 3/2 PP PPP PP PageRank briefly Pinitial provides the initial potential values, given by the available data The matrix W assigns values of links between cells 1 1/32/3 1            003/1 103/2 010 W  321initialP
  15. 15. Creation of functional areas Using the archaeological findings we draw up the functional areas, i.e. levels of spatial and functional organization (e.g. urban, suburban, rural areas) in which the urban space is organized. Archaeological findings Functional area
  16. 16. Geomorphological, stratigraphic geophisical and archaeological  data have been processed using geostatistical techniques in order  to get temporal DEMs for 7 periods from Protohistory to  Contemporary Age and to create  paleogeographical maps Creation of paleogeographical maps
  17. 17. Contemporary Age Modern Age Late Medieval period Early Medieval period Roman period Etruscan period Protohistory
  18. 18. THE MAP OF ARCHAEOLOGICAL POTENTIAL OF THE URBAN AREA OF PISA The map of archaeological potential is given by the composition of the 7 layers. It’s a weighted sum in which archaeological periods with less information available are taken more into account.
  19. 19. [www.archaide.eu] The ArchAIDE project goes exactly in this direction. 
  20. 20. ArchAIDE aims to support the classification and interpretation work of the  archaeologists with innovative computer‐based tools, able to provide the user with  features for matching of each discovered sherd over the huge existing ceramic catalogues. Goals from to
  21. 21. [How?]
  22. 22. From drawings digitisationto 3D models
  23. 23. [algorythms][algorythms]
  24. 24. Appearance‐based recognition Training set of 15.000images  On 77%the first result is the right one On 95%the right one is within the top 5results  gg1
  25. 25. Diapositiva 26 gg1 gabriele gattiglia; 12/02/2018
  26. 26. 1 3 Ranking of  pottery  classes by  relevance 2 Shape‐basedrecognition The neural network has been trained both with real and syntetic data Preliminary results say that the right result is within the top 5 on the  78%
  27. 27. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics
  28. 28. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics
  29. 29. Just one minute…..
  30. 30. ArchAIDE Archaeological Automatic Interpretation and  Documentation of cEramics [Big Data]
  31. 31. https://www.flickr.com/photos/wwworks/
  32. 32. This project has received funding from the European Union’s Horizon 2020 research  and innovation programme under grant agreement N.693548 The views and opinions expressed in this presentation are the sole responsibility of the authors and do not necessarily  reflect the views of the European Commission. [Thank you for your attention] www.archaide.eu

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