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Computational archeology platform

GINYS-ICAC-006
Hector A. Orengo
Investigador responsable
ORCID | PRC page
The Computational Archeology Laboratory is a facility created to support researchers interested in the development of computational applications for the study of the past: geospatial applications (remote sensing and GIS), geostatistics, 3D reconstruction and modeling, network analysis, simulation and artificial intelligence applications (machine learning, deep learning).

Services

Members of this lab collaborate in the design and development of specific computer applications for research projects in archaeology.

Equipment

  • Server adaptable a different types of computing operations and data processing (Supermicro 4U Dual AMD EPYC 32, 500GB RAM, 2 GPUs Nvidia A40 with 48GB each)
  • Computers specialized in photogrammetry and GIS (with 64 GB RAM and GPUs Nvidia of 12 GB RAM each)
  • A3 optical scanner for photogrammetry
  • High resolution (sub-millimeter) automated metrological 3D scanner

Staff

Investigador responsable
Hector A. Orengo | horengo@icac.cat | 977 24 91 33 | ORCID | PRC Page

Publications

Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data
Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A.
Proceedings of the National Academy of Sciences, 117(31), 2020
DOI: 10.1073/pnas.2005583117
Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia
Berganzo-Besga, I., Orengo, H. A., Lumbreras, F., Carrero-Pazos, M., Fonte, J., & Vilas-Estévez, B.
Remote Sensing, 13(20), 2021
DOI: 10.3390/rs13204181
New developments in drone-based automated surface survey: Towards a functional and effective survey system
Orengo, H. A., Garcia-Molsosa, A., Berganzo-Besga, I., Landauer, J., Aliende, P., & Tres-Martínez, S.
Archaeological Prospection, 28(4), 2021
DOI: 10.1002/arp.1822
Potential of deep learning segmentation for the extraction of archaeological features from historical map series
Garcia-Molsosa, A., Orengo, H. A., Lawrence, D., Philip, G., Hopper, K., & Petrie, C. A.
Archaeological Prospection, 28(2), 2021
DOI: 10.1002/arp.1807

INSTITUTE

ICAC - Inst. Català d'Arqueologia Clàssica

Pl. Rovellat, s/n

43003 Tarragona http://www.icac.cat/

DOMAINS RIS3CAT

  • Cultural system
  • Education and knowledge generation system
  • Industrial system

CATEGORIES

  • TECHNOLOGICAL EQUIPMENT
    • Archeology, Geology and Paleontology Laboratories

RATES AND ACCESS

Availability Level: Low
Access Procedures:
Open

QUALITY CONTROL

HRS4R
Human Resource Strategy for Researchers It supports research institutions and funding organizations in implementing the Charter and Code in their policies and practices. The implementation of the Charter and Code principles by research institutions makes them more attractive to researchers looking for a new employer or host for their research project..
Website link
Code of Conduct CERCA
The CERCA Code of Conduct, aligned with the European Code of Conduct for Research Integrity, recognized by the European institutions, provides guarantees in terms of transparency, open access to data and results, industrial property management, scientific integrity and relationship with the media, among others.
Website link
Status: Planned
CERCA - Ginys