Iban Berganzo‐besga

Biographic Data

ID4920975
NAMEIban Berganzo‐besga
GIVEN NAMESIban
FAMILY NAMEBerganzo‐besga
SIGNATUREBESGA I B
AFFILIATIONSBarcelona Supercomputing Center
ORCID0000-0002-6161-2452
VERIFIEDYes
TOTAL WORKS8
TOTAL CITATIONS9
AUTHOR COUNT8
EDITOR COUNT0
FIRST PUBLICATION YEAR2021
LATEST PUBLICATION YEAR2026
H-INDEX2

  • Experts against automation? Comparing artificial intelligence and human identifications of multi-cell cereal husk phytoliths

    Open AccessMonica N Ramsey, Melanie Pugliese et al.ARTICLEJournal of Archaeological Science…2026

    This study compares the efficacy of a deep learning-based computer vision model against manual expert identification of multi-cell husk phytoliths from wheat ( Triticum boeticum/dicoccoides ), barley ( Hordeum spontaneum ), and oats ( Avena sativa ). Conducted via an online survey in 2024 with twelve respondents (92% completion rate, one respondent did not finish), the survey presented 18 phytolith images selected based on: 1) deep learning perfo…

  • Best practices for the application of computer vision-based machine learning in archaeology

    Open AccessIban Berganzo‐besga, Hector A Orengo et al.ARTICLEJournal of Archaeological Science2026

    Defines seven best practices for CV-based ML in archaeology. • Links sources, preprocessing, training, post-treatment and validation. • Shows how to reduce false positives in low-density survey settings. • Integrates explainability, cross-validation and ethical responsibility. • Promotes reproducible, FAIR and archaeologically informed AI workflows

  • Leveraging Big Multitemporal Multisource Satellite Data and Artificial Intelligence for the Detection of Complex and Invisible Features

    Open AccessNazarij Buławka, Hector A Orengo et al.ARTICLEArchaeological Prospection2026

    The detection of buried or obscured archaeological features remains a central challenge in landscape archaeology, particularly in the irrigated floodplains of Mesopotamia where levees and canals formed the basis of complex agrarian systems. This study presents a deep learning–based approach for the large‐scale, automated detection of ancient levees in central Iraq, integrating big multitemporal and multisource satellite datasets with advanced ins…

  • Archaeological Classification of Small Datasets Using Meta- and Transfer Learning Methods

    Open AccessDeniz Kayıkcı, Iban Berganzo-Besga et al.ARTICLEJournal of Computer Applications…2026

    In recent years, the increasing application of machine learning (ML) techniques to solve archaeological problems has garnered substantial interest. The relevant issue is that limited-sample, heterogeneous, and non-standard structures of datasets in archaeology affect the quality of deep learning (DL) model efficiency in training and testing. This study explores the effectiveness of artificial neural networks (ANNs) in addressing the unique challe…

  • Hidden in Plain Sight

    Open AccessC A Petrie, Junaid Abdul Jabbar et al.ARTICLEJournal of Field Archaeology2026References: 26

  • Deep learning-based detection of qanat underground water distribution systems using Hexagon spy satellite imagery

    Open AccessNazarij Buławka, Hector A Orengo et al.ARTICLEJournal of Archaeological Science2024Cited by: 4References: 7

    Qanats are a remarkable type of ancient hydraulic structure for sustainable water distribution in arid environments that use subterranean channels to transport water from highland or mountainous areas. The presence of the qanat system is marked by a line of regularly spaced shafts visible from the surface, which can be used to detect qanats using satellite imagery. Typically, qanats have been documented by field mapping or manual digitisation wit…

  • Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms

    Open AccessIban Berganzo-Besga, Iban Berganzo‐besga et al.ARTICLEJournal of Archaeological Science2022Cited by: 2References: 2

  • New developments in drone‐based automated surface survey

    Open AccessHector A Orengo, Arnau Garcia-Molsosa et al.ARTICLEArchaeological Prospection2021Cited by: 3References: 3

    This paper presents new developments on drone‐based automated survey for the detection of individual items or fragments of material culture visible on the ground surface. Since the publication of our original proof of concept, awarded with the Journal of Archaeological Science and Society for Archaeological Sciences Emerging Investigator Award 2019, additional funding has allowed us to implement a series of improvements to the method. These aim t…