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Machine learning for cave entrance detection in a Maya archaeological area
Character, L.D. Beach, Timothy Luzzadder-Beach, Sheryl Cook, Duncan Edward Schank, Cody Valdez, Fred Mallner, M.
Character, L.D.
Beach, Timothy
Luzzadder-Beach, Sheryl
Cook, Duncan Edward
Schank, Cody
Valdez, Fred
Mallner, M.
Abstract
Machine learning can offer an efficient method to identify and map caves, sinkholes, and other cave-like features (i.e. sinkholes, rockshelters, voids) using remotely sensed imagery. While there exists a body of work applying machine learning for sinkhole identification, little work exists for caves. In the densely forested and rugged Maya Lowlands, developing such a methodology can help archaeologists to identify previously unknown caves that may contain important archaeological materials. Here, we introduce a proof-of-concept project that uses random forest and lidar-derived landscape morphometrics to map caves and other cave-like features in northwest Belize. Several undocumented caves and cave-like features were identified in our study area based on model results. Next steps towards making a more robust version of this model include the addition of more training data and integration of a larger number of morphologic parameters. Based on the results described here as well as those in cited works focused on caves, we proposed machine learning as a first step in cave and cave-like feature identification, followed then by fieldwork and ground-truthing.
Keywords
Machine learning, Maya, caves, lidar, GIS, Remote sensing
Date
2024
Type
Journal article
Journal
Book
Volume
45
Issue
4
Page Range
416-438
Article Number
ACU Department
School of Arts and Humanities
Faculty of Education and Arts
Faculty of Education and Arts
Collections
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled
Notes
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