Machine learning for cave entrance detection in a Maya archaeological area
Journal article
Character, L.D., Beach, Timothy, Luzzadder-Beach, Sheryl, Cook, Duncan Edward, Schank, Cody, Valdez, Fred and Mallner, M.. (2024). Machine learning for cave entrance detection in a Maya archaeological area. Physical Geography. 45(4), pp. 416-438. https://doi.org/10.1080/02723646.2023.2261182
Authors | Character, L.D., Beach, Timothy, Luzzadder-Beach, Sheryl, Cook, Duncan Edward, Schank, Cody, Valdez, Fred and Mallner, M. |
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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 |
Year | 01 Jan 2024 |
Journal | Physical Geography |
Journal citation | 45 (4), pp. 416-438 |
Publisher | Taylor & Francis Ltd (UK) |
ISSN | 0272-3646 |
Digital Object Identifier (DOI) | https://doi.org/10.1080/02723646.2023.2261182 |
Web address (URL) | https://www.tandfonline.com/doi/full/10.1080/02723646.2023.2261182 |
Open access | Published as non-open access |
Research or scholarly | Research |
Page range | 416-438 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 11 Oct 2023 |
Publication process dates | |
Accepted | 21 Aug 2023 |
Deposited | 20 Aug 2024 |
Additional information | © 2023 Informa UK Limited, trading as Taylor & Francis Group. |
Place of publication | United Kingdom |
https://acuresearchbank.acu.edu.au/item/90wyv/machine-learning-for-cave-entrance-detection-in-a-maya-archaeological-area
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