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Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites

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Analysis of Images, Social Networks and Texts (AIST 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14486))

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Abstract

The paper proposes a methodology for solving the task of accurate semantic classification of 3D data using a combination of 2D and 3D methods based on the YOLO detector and the modified DGCNN network. The methodology is tested on the example of the problem of classification of large-scale geospatial objects, such as digital relief models of archaeological sites. A method for accurate registration of objects (FCIP) in the class of affine transformations using geometric and color features was proposed. The results of computer modeling of the proposed methodology based on FICP+DGCNN*+YOLO were presented and discussed. The methodology has theoretical and applied significance not only for the decryption and research of archaeological sites, but also for many applications of digital information processing and robotics in general.

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Acknolwledgments

The work was supported by the Russian Science Foundation, project no. 23-11-20007.

Author information

Authors and Affiliations

  1. Chelyabinsk State University, Chelyabinsk, Russia

    Aleksandr Vokhmintcev & Matvei Romanov

  2. Ugra State University, Khanty-Mansiysk, Russia

    Aleksandr Vokhmintcev & Andrey Melnikov

  3. Ufa University of Science and Technology, Ufa, Russia

    Olga Khristodulo

  4. South Ural State University, Chelyabinsk, Russia

    Matvei Romanov

Authors
  1. Aleksandr Vokhmintcev
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  2. Olga Khristodulo
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  3. Andrey Melnikov
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  4. Matvei Romanov
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Corresponding author

Correspondence to Aleksandr Vokhmintcev .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  2. Krasovskii Institute of Mathematics and Mechanics of Russian Academy of Sciences, Yekaterinburg, Russia

    Michael Khachay

  3. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  4. American University of Armenia, Yerevan, Armenia

    Habet Madoyan

  5. Artificial Intelligence Research Institute, Moscow, Russia

    Ilya Makarov

  6. University of Hamburg, Hamburg, Germany

    Irina Nikishina

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  8. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Maxim Panov

  9. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  10. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  11. Apptek, Aachen, Germany

    Evgenii Tsymbalov

  12. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

  13. MTS AI, Moscow, Russia

    Sergey Zagoruyko

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Vokhmintcev, A., Khristodulo, O., Melnikov, A., Romanov, M. (2024). Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites. In: Ignatov, D.I., et al. Analysis of Images, Social Networks and Texts. AIST 2023. Lecture Notes in Computer Science, vol 14486. Springer, Cham. https://doi.org/10.1007/978-3-031-54534-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-54534-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54533-7

  • Online ISBN: 978-3-031-54534-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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