Next week Melda KUCUKDEMIRCI from the Lund University Digital Archaeology Lab will co-chair a session at CHNT on "Artificial Intelligence: New pathways and towards cultural heritage"

Published 30 October 2020
banner CHNT 25 2020 Viienna

Next week Melda KUCUKDEMIRCI from the Lund University Digital Archaeology Lab -DARKLab- will co-chair a session at CHNT on "Artificial Intelligence: New pathways and towards cultural heritage". Among the speakers : Immo TRINKS | Alois HINTERLEITNER | Mario WALLNER | Klaus LÖCKER | Alexander BORNIK | Johannes HÖLLER |Matthias KUCERA |Roland FILZWIESER | Hannes SCHIEL| Tanja TRAUSMUTH| Alexandra VONKILCH| Jona SCHLEGEL| David RUSS| Wolfgang NEUBAUER. Follow the event !! https://www.chnt.at/

Conference on Cultural Heritage and New Technologies (CHNT) Artificial Intelligence: New pathways and towards cultural heritage

4-6 NOVEMBER 2020 – VIRTUAL

AI, ML AND DL IN SATELLITE, AERIAL AND GROUND BASED REMOTE SENSING

CHAIRS :   Apostolos SARRIS, Melda KUCUKDEMIRCI, Tuna KALAYCI

The recent years are experiencing an increasing number of Artificial Intelligence/Deep Learning/Machine Learning (AI/DL/ML) applications in various domains of Digital Humanities. Still, we are in the infant stage of their application and there are a number of limitations faced mainly due to the lack of a large volume of data that are required for their successful implementation.
Thanks to the development of UAVs, Lidar, Hyper-spectral and multispectral satellite imaging, multi-sensor and motorized equipment for geophysical surveys, a large quantity of data that cover extensive areas of the landscapes with high resolution is created, which can only be analysed efficiently by automated approaches leading towards a fast and accurate interpretation. This session is looking to build on the experience obtained from the application of various algorithms of AI/DL/ML on remote sensing data, spanning from satellite, aerial, ground based/geophysical and underwater surveys. We want to provide an exchange platform of the experiences acquired in image pre-processing, pixel-based classification, feature recognition, segmentation, and feature extraction and scene understanding in relation to the AI/DL/ML-based algorithms: comparison of networks, pre-trained models, hyper-parameter choices, APIs and in addition to these, discussion on obstacles, limitations, challenges, key bottlenecks and potential future direction of these new methodologies