Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA
The logistics, costs, and capacity needed to complete extensive archaeological pedestrian surveys to inventory cultural resources present challenges to public land managers. To address these issues, we developed a workflow combining lidar-derived imagery and deep learning (DL) models tailored for cu...
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| Language: | English |
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Cambridge University Press
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| Series: | Advances in Archaeological Practice |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2326376825000014/type/journal_article |
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| author | Claudine Gravel-Miguel Grant Snitker Jayde N. Hirniak Katherine Peck Alex Fetterhoff |
| author_facet | Claudine Gravel-Miguel Grant Snitker Jayde N. Hirniak Katherine Peck Alex Fetterhoff |
| author_sort | Claudine Gravel-Miguel |
| collection | DOAJ |
| description | The logistics, costs, and capacity needed to complete extensive archaeological pedestrian surveys to inventory cultural resources present challenges to public land managers. To address these issues, we developed a workflow combining lidar-derived imagery and deep learning (DL) models tailored for cultural resource management (CRM) programs on public lands. It combines Python scripts that fine-tune models to recognize archaeological features in lidar-derived imagery with denoising QGIS steps that improve the predictions’ performance and applicability. We present this workflow through an applied case study focused on detecting historic agricultural terraces in the Piedmont National Wildlife Refuge, Georgia, USA. For this project, we fine-tuned pretrained U-Net models to teach them to recognize agricultural terraces in imagery, identified the parameter settings that led to the highest recall for detecting terraces, and used those settings to train models on incremental dataset sizes, which allowed us to identify the minimum training size necessary to obtain satisfying models. Results present effective models that can detect most terraces even when trained on small datasets. This study provides a robust methodology that requires basic proficiencies in Python coding but expands DL applications in federal CRM by advancing methods in lidar and machine learning for archaeological inventorying, monitoring, and preservation. |
| format | Article |
| id | doaj-art-e09907d46dc44848ba4908a6667976c2 |
| institution | Kabale University |
| issn | 2326-3768 |
| language | English |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Advances in Archaeological Practice |
| spelling | doaj-art-e09907d46dc44848ba4908a6667976c22025-08-20T13:02:15ZengCambridge University PressAdvances in Archaeological Practice2326-376812410.1017/aap.2025.1Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USAClaudine Gravel-Miguel0https://orcid.org/0000-0003-1324-7937Grant Snitker1https://orcid.org/0000-0003-3548-2241Jayde N. Hirniak2https://orcid.org/0000-0001-6491-8097Katherine Peck3https://orcid.org/0009-0008-4068-2591Alex Fetterhoff4Cultural Resource Sciences Program, New Mexico Consortium, Los Alamos, NM, USA Department of Anthropology, University of New Mexico, Albuquerque, NM, USACultural Resource Sciences Program, New Mexico Consortium, Los Alamos, NM, USA Department of Anthropology, University of New Mexico, Albuquerque, NM, USAInstitute of Human Origins, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USACultural Resource Sciences Program, New Mexico Consortium, Los Alamos, NM, USA Department of Anthropology, University of New Mexico, Albuquerque, NM, USACultural Resource Sciences Program, New Mexico Consortium, Los Alamos, NM, USA Department of Anthropology, University of New Mexico, Albuquerque, NM, USAThe logistics, costs, and capacity needed to complete extensive archaeological pedestrian surveys to inventory cultural resources present challenges to public land managers. To address these issues, we developed a workflow combining lidar-derived imagery and deep learning (DL) models tailored for cultural resource management (CRM) programs on public lands. It combines Python scripts that fine-tune models to recognize archaeological features in lidar-derived imagery with denoising QGIS steps that improve the predictions’ performance and applicability. We present this workflow through an applied case study focused on detecting historic agricultural terraces in the Piedmont National Wildlife Refuge, Georgia, USA. For this project, we fine-tuned pretrained U-Net models to teach them to recognize agricultural terraces in imagery, identified the parameter settings that led to the highest recall for detecting terraces, and used those settings to train models on incremental dataset sizes, which allowed us to identify the minimum training size necessary to obtain satisfying models. Results present effective models that can detect most terraces even when trained on small datasets. This study provides a robust methodology that requires basic proficiencies in Python coding but expands DL applications in federal CRM by advancing methods in lidar and machine learning for archaeological inventorying, monitoring, and preservation.https://www.cambridge.org/core/product/identifier/S2326376825000014/type/journal_articleconvolutional neural network (CNN)cultural resource management (CRM)deep learninglidarterracesU-Netred neuronal convolucional (RNC)programas de gestión de recursos culturales (PGRC)aprendizaje profundolidarterrazasU-Net |
| spellingShingle | Claudine Gravel-Miguel Grant Snitker Jayde N. Hirniak Katherine Peck Alex Fetterhoff Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA Advances in Archaeological Practice convolutional neural network (CNN) cultural resource management (CRM) deep learning lidar terraces U-Net red neuronal convolucional (RNC) programas de gestión de recursos culturales (PGRC) aprendizaje profundo lidar terrazas U-Net |
| title | Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA |
| title_full | Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA |
| title_fullStr | Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA |
| title_full_unstemmed | Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA |
| title_short | Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA |
| title_sort | semantic segmentation of archaeological features on public lands case study of historical cotton terraces within the piedmont national wildlife refuge georgia usa |
| topic | convolutional neural network (CNN) cultural resource management (CRM) deep learning lidar terraces U-Net red neuronal convolucional (RNC) programas de gestión de recursos culturales (PGRC) aprendizaje profundo lidar terrazas U-Net |
| url | https://www.cambridge.org/core/product/identifier/S2326376825000014/type/journal_article |
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