and ground based remote sensing
Call for Paper
Apostolos SARRIS (1) | Melda KÜÇÜKDEMİRCİ (2) | Tuna KALAYCI (3)
((1) University of Cyprus and GeoSat Research Lab, Institute for Mediterranean Studies-FORTH, Cyprus / Greece | (2) Istanbul University Cerrahpaşa, Turkey | (3) Faculty of Archaeology, Leiden University, The Netherlands)
Keywords: Articificial Intelligence, Machine Learning, Remote Sensing, Satellite, Geophysics, Aerial
Call: 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.
Submission (open April 15, 2020)
Mind the guidelines