Artificial Intelligence, Machine Learning, and Deep Learning in Archaeology
Event Information
Description
An international conference and workshop on 7-8 November 2019 in Rome, Italy. Hosted by the British School at Rome and the European Space Agency.
Artificial intelligence, machine learning and deep learning are opening new frontiers of inquiry. Join the BSR and ESA in exploring applications of machine learning in artifact analysis, text mining and remote sensing. Papers will be presented at the BSR on November 7, followed by a workshop at ESA's European Space Research Institute on November 8.
Program
For PDF including abstracts see: http://www.bsr.ac.uk/site2014/wp-content/uploads/2019/11/Machine_Learning_Archaelogy_191023.pdf
Day 1 Venue: British School at Rome (BSR) 9:00-19:00
9:00-9:05 Saluti by BSR Director Stephen Milner
9:05-9:10 Introduction by Peter Campbell, Chris Stewart, and Iris Kramer
Session 1: Archaeology and Culture
9:10-9:30 Traviglia, Arianna and Marco Fiorucci Graph Convolutional Neural Networks for Cultural Heritage: Applications in RS recognition, numismatics and epigraphy
9:30-9:50 Gattiglia, Gabriele, and Francesca Anichini ArchAIDE: A Neural Network for automated recognition of archaeological pottery
9:50-10:10 Tziotas, Christos Machine Learning for the Classification of Stone-Age Artefacts
10:10-10:30 Palomeque-Gonzalez, Juan F. Techniques of Machine learning for sex determination in human remains: When more advanced doesn't mean better
Coffee break 10:30-11:00
Session 2: Archaeology and Culture
11:00-11:20 Brandsen, Alex, Karsten Lambers, Suzan Verberne, and Milco Wansleeben Using Machine Learning for Named Entity Recognition in Dutch Excavation Reports
11:20-11:40 Evans, Damian Tracing Large-Scale Archaeological and Environmental Legacies of Tropical Forest Societies
11:40-12:00 Graham, Shawn and Damien Huffer Digital Phrenology? An Experimental Digital Archaeology
12:00-12:20 Sommerschield, Thea and Yannis Assael Restoring ancient text using deep learning: a case study on Greek epigraphy
12:20-13:00 Discussion
Lunch 13:00-14:00
Session 3: Remote Sensing 1 (LiDAR)
14:00-14:20 Moreno Escobar, Maria del Carmen and Saul Armendariz Historical landscapes and Machine Learning: (Re)Creating the hinterland of Tarragona, Spain
14:20-14:40 Schneider, Agnes Learning to See LiDAR Pixel-by-Pixel
14:40-15:00 Somrak, Maja, Žiga Kokalj, and Sašo Džeroski Classifying objects from ALS- derived visualizations of ancient Maya settlements using convolutional neural networks
15:00-15:20 Verschoof-van der Vaart, Wouter Baernd and Karsten Lambers The use of R- CNNs in the automated detection of archaeological objects in LiDAR data
15:20-15:40 Trier, Øivind Due and Kristian Løseth Automated detection of grave mounds, deer hunting systems and charcoal burning platforms from airborne lidar data using faster- RCNN
Keynote Lecture by Barbara McGillivray
15:40-16:40 Tracking changes in meaning over time: how can machines learn from humans
16:40-17:00 Discussion
20:00 Private Dinner and Drinks Reception for Conference Presenters at Villa Wolkonsky
Day 2 Venue: European Space Agency (ESA) Centre for Earth Observation 10:00-17:00
Session 4: Remote Sensing 2 (machine learning for geospatial analysis in cultural heritage)
09:30-10:00 Chris Stewart Welcome to ESA/ESRIN
10:00-11:00 Keynote: Juan A. Barceló Big Data Sources and Deep Learning Methods in Archaeology: A critical overview
11:00-11:20 Coffee Break
11:20-11:40 Remondino, Fabio, Emre Ozdemir, Eleonora Grilli Classification of Heritage 3D Data with Machine and Deep Learning Strategies
11:40-12:00 Kramer, Iris, Jonathon Hare, and Dave Cowley Arran: a benchmark dataset for automated detection of archaeological sites on LiDAR data
12:00-12:20 Chris Stewart Machine Learning with Earth Observation for Cultural Heritage at the ESA Phi-Lab
12:20-12:40 Marsella, M.A., J.F. Guerrero Tello, and A. Celauro Deep learning for automatic feature detection and extraction on the archaeological landscape of Centocelle neighborhood in Rome using optical and radar remote sensing images
12:40-13:00 Karamitrou, Alexandra and Fraser Sturt Detection of Archaeological Sites using Artificial Intelligence and Deep Learning Techniques
13:00-14:00 Lunch
14:00-14:20 Rayne, Louise Mapping Threats to Cultural Heritage of the Middle East and North Africa
14:20-14:40 el-Hajj, Hassan InSAR Coherence Patch Classification using ML: Towards Automatic Looting Detection of Archaeological Sites
14:40-15:00 Küçükdemirci, Melda and Apostolos Sarris U-net for Archaeo-Geophysical Image Segmentation
15:00-15:20 Linstead, Erik, Alice Gorman, and Justin St. P. Walsh Machine Learning in Space Archaeology
15:20-15:40 Orengo, Hector A., Arnau Garcia-Molsosa, Francesc C. Conesa, Cameron A. Petrie As above so below: artificial intelligence-based detection and analysis of archaeological sites and features at a continental scale
15:40-16:00 Discussion
16:00-16:20 Coffee break
16:20-17:20 Visit to Phi-Experience
Optional November 9 Tour
Visit to Archaeological Sites
TRANSPORTATION TO ESA/ESRIN (8 NOV)
The easiest way to travel to ESA/ESRIN from Rome is by train: https://www.trenitalia.com/.
Transportation by train to ESA/ESRIN from Rome:
08:07 Roma Termini to 08:25 Tor Vergata (train to Frosinone)
08:35 Roma Termini to 08:53 Tor Vergata (train to Cassino)
09:14 Roma Termini to 09:32 Tor Vergata (train to Frosinone)
Transportation by train to Rome from ESA/ESRIN:
16:48 Tor Vergata to 17:13 Roma Termini
17:27 Tor Vergata to 17:48 Roma Termini
17:50 Tor Vergata to 18:13 Roma Termini
For other transportation options, please see: https://www.esa.int/About_Us/ESRIN/Getting_to_ESRIN