January 2022 Virtual Meeting

Monday, January 10, 2022
6:00 - 7:30 pm EST

Note: ETGS members will receive an email with info for logging into the meeting.

January Presentation

Archaeological Machine Learning: Using remotely sensed imagery to find and map archaeological features



Leila Character (previously Donn)1, Tim Beach1, Cody Schank1, Takeshi Inomata2, Agustin Ortiz JR3, Adam Rabinowitz4, Tom Garrison1

1. leiladonn@utexas.edu, Department of Geography and the Environment, University of Texas at Austin
2. School of Anthropology, University of Arizona
3. Underwater Archaeology Branch, Naval History and Heritage Command
4. Department of Classics, University of Texas at Austin



We are creating a series of supervised machine learning models to predict and map the locations of unknown or unmapped archaeological features using remotely sensed imagery. The goal of this work is to create an efficient, cost-effective, and replicable method of rapidly mapping archaeological sites, including those that are very large. This project began in 2018 with the goal of creating a targeted method of finding cave entrances at Maya archaeological sites located in the dense tropical forests of Guatemala and Belize. In 2019, we used a random forest classifier, airborne laser scanning (ALS) data, and a training dataset of known caves to successfully identify several previously undocumented caves in northwestern Belize. Two of these caves contained archaeological materials. Building on this work, modeling has been expanded to include other types of hidden and obscured features that colleagues are interested in studying. These include ancient Maya archaeological features in Guatemala and Mexico, shipwrecks off the coast of the United States, and ancient burial mounds in Romania. The models for the archaeological features take ALS, sonar, and multispectral imagery as input, are based on existing convolutional neural network architectures, and make use of transfer learning. These models can be used to create more accurate maps of archaeological features to aid management objectives, study patterns across the landscape, and find new features. Such models can easily be adjusted to identify other types of features and accept different types of imagery as input. This work seeks to make machine learning methods accessible to non-computer scientists interested in study, management, and conservation of archaeological heritage.


Leila Character (previously Donn) is a PhD Candidate in the Department of Geography and the Environment at the University of Texas at Austin focused on using machine learning and remotely sensed imagery to find and map archaeological features. Her work seeks to create an efficient, cost-effective, and replicable method of rapidly mapping archaeological sites, including those that are very large. Leila's work includes partners from academia, federal government, and private industry. Her current projects include a shipwreck identification model being completed in partnership with the US Navy, a Maya archaeological feature identification model being completed in partnership with a group of archaeologists that work in Guatemala and Mexico, and an ancient burial mound identification model being completed with a group of archaeologists that work in Romania. She has also partnered with an artificial intelligence start-up on a project to create a multi-species tree and health status classifier using hyperspectral imagery collected by drone. Prior to beginning her PhD, she completed a master's degree in the same department focused on the use of lidar and geoarchaeological methods to study the land-use patterns of the ancient Maya in north-central Belize. Her B.S. is in geology (from Sewanee: The University of the South in Tennessee) with a minor in anthropology focused on archaeology. Between receiving her B.S. and M.A. she worked for five years as a geologist and environmental scientist in Alaska, Texas, and Tennessee. You can contact Leila at leiladonn@utexas.edu, check out her website at https://leilacharacter.wixsite.com/leilacharacter, and follow her on Instagram for research updates at @leilacharacter.



Greetings! We hope you will join us for the next ETGS virtual meeting, and that you, your family, and your colleagues are staying healthy and well. 

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Thank you for your patience and understanding as we continue adapting to this virtual format. As always, we welcome and appreciate your feedback and suggestions for improvement.




Page updated December 14, 2021