2026-09-20T16:11:21Zhttps://api.mountainscholar.org/server/oai/request

oai:mountainscholar.org:10217/2418242025-12-30T03:17:13Zcom_10217_100532com_10217_100000com_10217_100326com_10217_100303col_10217_182111col_10217_100327

Redford, Nicholas, author Fisher, Chris, advisor Tulanowski, Elizabeth, committee member Leisz, Stephen J., committee member 2025-09-01T10:42:20Z 2025-09-01T10:42:20Z 2025 Redford_colostate_0053N_19218.pdf https://hdl.handle.net/10217/241824 https://doi.org/10.25675/3.02144 Archaeological landscapes face increasing threats from climate change and human activity, necessitating scalable methods for site detection. LiDAR has revolutionized archaeological surveys by revealing features beneath dense vegetation, but manual interpretation remains labor-intensive. This study introduces an Automated Archaeological Survey Method (AASM) that utilizes machine learning to analyze tree canopy structures as proxies for underlying archaeological features. Using a LiDAR-derived digital canopy model (DCM), the Bi-path Ensemble (BPE) model predicts the locations of the land-use typologies of "public" and "private" space at Angamuco. The model evaluation shows moderate to high agreement between predicted and true labels, particularly for dense public and un-terraced private spaces. These results suggest that vegetation patterns can serve as reliable indicators of past human activity, offering a scalable approach for prioritizing areas for archaeological surveys. By integrating creative computational methods with remote sensing data, this study advances the use of machine learning in archaeological landscape reconstruction. born digital masters theses English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Towards reconstructing cities with AI: a novel machine learning approach for automated archaeological surveying and preservation by learning canopy structures Text Master of Arts (M.A.) Masters Anthropology and Geography Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access