Computational archaeology thesis defence: Iban Berganzo-Besga

Iban Berganzo-Besga

As part of the interuniversity doctoral program in Classical Archeology (URV-ICAC-UAB), we are pleased to invite you to the public reading of the doctoral thesis of GIAP researcher Iban Berganzo-Besga, which has been co-directed by Prof. Hèctor A. Orengo (ICREA Research Professor at ICAC) and Felipe Lumbreras (Computer Vision Center of the Autonomous University of Barcelona, CVC-UAB):

New Computational Methods for Automated Large-Scale Archaeological Site Detection

The defence will be hosted in ICAC’s Auditorium on Friday, March 10th, at 11h CET.

  • Dra. Rachel Opitz, Senior Lecturer a la Universitat de Glasgow (School of Humanities)
  • Dr. Arnau Garcia Molsosa, Ramón y Cajal researcher at ICAC
  • Dr. Robert Benavente, associate professor at the Computer Vision Center (CVC) of the Autonomous University of Barcelona


This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology.

Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’s specific challenges have been designed.

These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision achieved in previous detection studies and how to handle problems with little amount of training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies.

Likewise, this doctoral thesis has contributed to the development of a Computational Archaeology Laboratory, which has allowed us to build these models, with which large amounts of data, never before achieved in archaeological site detection studies or survey work, have been obtained. To sum up, this PhD presents a comprehensive guide on the design, application and validation of methods for automated large-scale archaeological site detection within the field of computational archaeology.

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