A new study published in the Journal of Archaeological Science shows that it is possible to automate the detection and classification of phytoliths with a high-level of accuracy, up to a species level. This method has the potential to allow the development of much larger analytical datasets in a fraction of the time than was…
Read more
The CAA2022 (Annual Conference of Computer Applications and Quantitative Methods in Archaeology) that will be held from Aug 3rd to 8th in Oxford (UK), in a hybrid format. GIAP (ICAC) researchers are leading sessions #13 and #14. Check the abstracts and the full programme at the conference’s website: https://2022.caaconference.org/programme/ 10-11/08/22Session 13. Machine and deep learning methods in archaeological…
Read more
We are pleased to invite you to contribute to our sessions in the CAA2022 (Annual Conference of Computer Applications and Quantitative Methods in Archaeology) that will be held from Aug 3rd to 8th in Oxford (UK), in a hybrid format. GIAP-ICAC researchers are leading two sessions: S13: Machine and deep learning methods in archaeological research:…
Read more
We are searching for excellent candidates with a PhD in Computer Science, Remote Sensing or a similar field interested in conducting research in archaeological sites detection based on applying machine learning techniques on multispectral aerial images. Contract duration: 22 months The call will open in the forthcoming weeks through Working at the ICAC/Open calls. The context…
Read more
Today, 6th Nov 2021 at 13.45 CET, Giannis Apostolou (GIAP-ICAC) presents his latest research in the International Conference of Postgraduate Students of History and Archaeology organised by the International Hellenic University: “INTERPRETING IDENTITIES IN THE EASTERN MEDITERRANEAN AND THE BLACK SEA” Abstract: Artificial Intelligence and the archaeology of Greek landscapes The rise of Convolutional Neural…
Read more
Image: detected tumuli in Galicia (Spain): (a) point distribution; (b) heat map. Author: Iban Berganzo. Archaeological tumuli are one of the most common types of archaeological sites and can be found across the globe. This is perhaps why many studies have attempted to develop methods for their automated detection. Their characteristic tumular shape has been…
Read more
Recently, an article in La Vanguardia highlighted our projects in Galicia (Spain), where we have been using Artificial Intelligence and Deep Learning to automatise the detection of archaeological features, structures and sites. With this post, we wanted to provide insight on this transformative methodology. Artificial intelligence (AI) is being considered the fourth industrial revolution. Derived from engineering…
Read more
