1. Machine learning for cave entrance detection in a Maya archaeological area.
- Author
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Character, L.D., Beach, T., Luzzadder-Beach, S, Cook, D., Schank, C., Valdez Jr., F., and Mallner, M.
- Subjects
MAYAS ,CAVES ,RANDOM forest algorithms ,MACHINE learning ,SPELEOTHEMS ,SINKHOLES ,PROOF of concept ,STALACTITES & stalagmites ,LANDSCAPE assessment - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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