TY - GEN
T1 - Machine Learning and Digital Heritage
T2 - 4th International Congress on Information and Communication Technology, ICICT 2019
AU - Belhi, Abdelhak
AU - Gasmi, Houssem
AU - Bouras, Abdelaziz
AU - Alfaqheri, Taha
AU - Aondoakaa, Akuha Solomon
AU - Sadka, Abdul H.
AU - Foufou, Sebti
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2020.
PY - 2020
Y1 - 2020
N2 - Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.
AB - Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.
KW - 3D-holoscopic imaging
KW - Artificial intelligence
KW - CEPROQHA project
KW - Cultural heritage
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85078425264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078425264&partnerID=8YFLogxK
U2 - 10.1007/978-981-32-9343-4_29
DO - 10.1007/978-981-32-9343-4_29
M3 - Conference contribution
AN - SCOPUS:85078425264
SN - 9789813293427
T3 - Advances in Intelligent Systems and Computing
SP - 363
EP - 374
BT - 4th International Congress on Information and Communication Technology - ICICT 2019, London
A2 - Yang, Xin-She
A2 - Sherratt, Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer
Y2 - 27 February 2019 through 28 February 2019
ER -