Yes, it’s been more than a year since my last post. It’s time to get this blog going again! I am sorry for my absence.
I’ve been following the Artificial Intelligence, Machine Learning and Deep Learning in Archaeology Conference that took place in Rome remotely via Twitter. I had to do it remotely as I only found out about it when it had already started. This post aims to bring visibility to the conference in order for people like me to get an understanding of what kinds of work were presented ( and to not to miss it next time 🙂 ).
The conference was organised by the British School at Rome and the European Space Agency and took place in Rome (<3) on the 7th and 8th of November 2019. You can check out some details on the ticket site and their website.
- Traviglia, Arianna and Marco Fiorucci Graph Convolutional Neural Networks for Cultural Heritage: Applications in RS recognition, numismatics and epigraphy
- Gattiglia, Gabriele, and Francesca Anichini ArchAIDE: A Neural Network for automated recognition of archaeological pottery
- Tziotas, Christos Machine Learning for the Classification of Stone-Age Artefacts
- Palomeque-Gonzalez, Juan F. Techniques of Machine learning for sex determination in human remains: When more advanced doesn’t mean better
- Brandsen, Alex, Karsten Lambers, Suzan Verberne, and Milco Wansleeben Using Machine Learning for Named Entity Recognition in Dutch Excavation Reports
- Evans, Damian Tracing Large-Scale Archaeological and Environmental Legacies of Tropical Forest Societies
- Graham, Shawn and Damien Huffer Digital Phrenology? An Experimental Digital Archaeology
- Sommerschield, Thea and Yannis Assael Restoring ancient text using deep learning: a case study on Greek epigraphy
- Moreno Escobar, Maria del Carmen and Saul Armendariz Historical landscapes and Machine Learning: (Re)Creating the hinterland of Tarragona, Spain
- Schneider, Agnes Learning to See LiDAR Pixel-by-Pixel
- Somrak, Maja, Žiga Kokalj, and Sašo Džeroski Classifying objects from ALS- derived visualizations of ancient Maya settlements using convolutional neural networks
- Verschoof-van der Vaart, Wouter Baernd and Karsten Lambers The use of R- CNNs in the automated detection of archaeological objects in LiDAR data
- 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 Tracking changes in meaning over time: how can machines learn from humans
- Chris Stewart Welcome to ESA/ESRIN
- Keynote: Juan A. Barceló Big Data Sources and Deep Learning Methods in Archaeology: A critical overview
- Remondino, Fabio, Emre Ozdemir, Eleonora Grilli Classification of Heritage 3D Data with Machine and Deep Learning Strategies
- Kramer, Iris, Jonathon Hare, and Dave Cowley Arran: a benchmark dataset for automated detection of archaeological sites on LiDAR data
- Chris Stewart Machine Learning with Earth Observation for Cultural Heritage at the ESA Phi-Lab
- 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
- Karamitrou, Alexandra and Fraser Sturt Detection of Archaeological Sites using Artificial Intelligence and Deep Learning Techniques
- Rayne, Louise Mapping Threats to Cultural Heritage of the Middle East and North Africa
- el-Hajj, Hassan InSAR Coherence Patch Classification using ML: Towards Automatic Looting Detection of Archaeological Sites
- Küçükdemirci, Melda and Apostolos Sarris U-net for Archaeo-Geophysical Image Segmentation
- Linstead, Erik, Alice Gorman, and Justin St. P. Walsh Machine Learning in Space Archaeology
- 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
For more information you can check out the abstracts here.
Hopefully you and I can find out about this in time next year to go listen to some super interesting talks 🙂