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