Remote Sensing and AI Coupled Approach for Large-Scale Archaeological Mapping in the Andean Arid Highlands: Case Study in Altos Arica, Chile

Castiello, Maria Elena; Landauer, Jürgen; Saintenoy, Thibault

doi:10.3390/rs17203499

Cited by 2Archaeological Research and ProtectionArchaeology and Cultural HeritageImage Processing and 3D Reconstruction

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Remote Sensing and AI Coupled Approach for Large-Scale Archaeological Mapping in the Andean Arid Highlands: Case Study in Altos Arica, Chile

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Castiello, Maria Elena, Landauer, Jürgen, Saintenoy, Thibault·2025·Remote Sensing

DOI 10.3390/rs17203499

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Abstract

Artificial intelligence algorithms for automated archaeological site detection have been scarcely applied in the Andean highlands, regions that preserve a significant amount of surface archaeological architecture but have not yet been fully explored or mapped due to the difficult terrain. This paper presents a case study of the application of convolutional neural networks (CNNs) to automatically identify archaeological architecture in the Azapa valley in the Arica y Parinacota region of Chile. Using a high-resolution and big regional-scale archaeological geodatabase created through a systematic and detailed photo-interpretation survey of satellite imagery and fieldwork, our study demonstrates the efficiency of CNN-based automated detection in identifying archaeological stone structures such as roundhouses and corrals in the Chilean highlands. After outlining the technical protocol for automated detection, we present the results and discuss the potential of our AI model for archaeological mapping in arid highland environments, from a regional to a more extended and global perspective.

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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
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Chicco, D., & Jurman, G. (2020) · BMC Genomics · doi:10.1186/s12864-019-6413-7 · Cited by 6,143
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399
Random Erasing Data Augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7000 · Cited by 3,617
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I. S., & Xie, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01548 · Cited by 1,791
Fastai: A Layered API for Deep Learning
Howard, J., & Gugger, S. (2020) · Information · doi:10.3390/info11020108 · Cited by 1,055
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Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., Wang, G., Shi, P., Khatteli, H., Chen, F., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111280 · Cited by 323
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Espinoza, J. C., Garreaud, R., Poveda, G., Arias, P. A., Molina‐Carpio, J., Masiokas, M. H., Viale, M., & Scaff, L. (2020) · Frontiers in Earth Science · doi:10.3389/feart.2020.00064 · Cited by 241
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Lasaponara, R., & Masini, N. (2011) · Journal of Archaeological Science · doi:10.1016/j.jas.2011.02.002 · Cited by 161
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Trier, Ø. D., Cowley, D. C., & Waldeland, A. U. (2018) · Archaeological Prospection · doi:10.1002/arp.1731 · Cited by 140
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Climate and culture change: exploring Holocene transitions
Anderson, D. G., Maasch, K. A., Sandweiss, D. H., & Mayewski, P. A. (2007) · Elsevier eBooks · doi:10.1016/b978-012088390-5.50006-6 · Cited by 70
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Borsdorf, A., & Stadel, C. (2015) · Springer geography · doi:10.1007/978-3-319-03530-7 · Cited by 52
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On the Airborne Lidar Contribution in Archaeology: from Site Identification to Landscape Investigation
Masini, N., Coluzzi, R., & Lasaponar, R. (2011) · InTech eBooks · doi:10.5772/14655 · Cited by 33
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Branca, D., & Haller, A. (2021) · Sustainability · doi:10.3390/su13116438 · Cited by 28
Machine learning for automatic detection of historic stone walls using LiDAR data
Trotter, E. F. L., Fernandes, A. C. M., Fibæk, C. S., & Keßler, C. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2057206 · Cited by 20
Machine learning-ready remote sensing data for Maya archaeology
Kokalj, Ž., Džeroski, S., Šprajc, I., Štajdohar, J., Draksler, A., & Somrak, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02455-x · Cited by 20
Mapping stone walls in Northeastern USA using deep learning and LiDAR data
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Mader, C., Godde, P., Behl, M., Binder, C., Hägele, E., Isla, J., Leceta, F., Lyons, M., Marsh, E., Odenthal, R., et al. (2024) · Frontiers in Environmental Archaeology · doi:10.3389/fearc.2024.1328315 · Cited by 18
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True, D. L., Lautaro Núñez, A., & Patricio Núñez, H. (1970) · American Antiquity · doi:10.2307/278146 · Cited by 17
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Self-citation
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Saintenoy, T., Ajata, R., Romero Guevara, A. L., & Sepúlveda, M. (2017) · Estudios Atacamenos · doi:10.4067/s0718-10432017005000001 · Cited by 16
Self-citation
Advances in remote sensing of the early Anthropocene in tropical wetlands: From biplanes to lidar and machine learning
Doyle, C. S., Luzzadder‐Beach, S., & Beach, T. (2022) · Progress in Physical Geography Earth and Environment · doi:10.1177/03091333221134185 · Cited by 15
Exploratory Analysis Using Deep Learning for Water-Body Segmentation of Peru’s High-Mountain Remote Sensing Images
Perez-Torres, W. I., Uman-Flores, D. A., Quispe-Quispe, A. B., Palomino-Quispe, F., Bezerra, E. S., Leher, Q. O., Paixão, T., & Alvarez, A. B. (2024) · Sensors · doi:10.3390/s24165177 · Cited by 14
A Multi-Temporal Analysis of Archaeological Site Destruction using Landsat Satellite Data and Machine Learning, Moche Valley, Peru
Payntar, N. D. (2023) · Journal on Computing and Cultural Heritage · doi:10.1145/3586079 · Cited by 11
Safeguarding Indigenous Heritage in the Chilean Atacama Desert: Negotiating Identity Claims and Community Perceptions of Long-term Climate Change
Haboucha, R., & Jofré, D. (2021) · Heritage & Society · doi:10.1080/2159032x.2022.2110375 · Cited by 9
Topological insights into the diachrony of ancient road networks: exploratory predictive modelling in the andean highlands
Saintenoy, T., Llobera, M., Thiéry, N. M., Crespo Fernández, M., Fábrega-Álvarez, P., & Santos, R. (2025) · Journal of Archaeological Science · doi:10.1016/j.jas.2024.106125 · Cited by 9
Self-citation
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Self-citation
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Guo, J., Zimmer-Dauphinee, J. R., Nieusma, J. M., Lu, S., Liu, Q., Deng, R., Cui, C., Yue, J., Lin, Y., Yao, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2504.20303 · Cited by 7
Machine learning for cave entrance detection in a Maya archaeological area
Character, L., Beach, T., Luzzadder‐Beach, S., Cook, D., Schank, C., Valdez, F., & Mallner, M. (2023) · Physical Geography · doi:10.1080/02723646.2023.2261182 · Cited by 7
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Arkush, E., Kohut, L. E., Housse, R., Smith, R. D., & Wernke, S. A. (2024) · Antiquity · doi:10.15184/aqy.2023.178 · Cited by 7
Beyond inca roads: the redes andinas project explores complex palimpsests of andean road networks
Saintenoy, T. (2024) · Antiquity · doi:10.15184/aqy.2024.84 · Cited by 6
Self-citation
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Landauer, J., Maddison, S., Fontana, G., & Posluschny, A. (2025) · Journal of Computer Applications in Archaeology · doi:10.5334/jcaa.178 · Cited by 5
Self-citation
Andean landscape legacies: comprehensive remote sensing mapping and GIS analysis of long-term settlement and land use for sustainable futures (NW argentina)
Lazzari, M., Oltean, I., Oyaneder Rodríguez, A., Scattolin, M. C., & Pereyra Domingorena, L. (2024) · Remote Sensing · doi:10.3390/rs16203795 · Cited by 4
Territorialité et fortifications entre la côte Pacifique et l'altiplano méridional andin à l'Intermédiaire Récent (1100 - 1450 Apr. J.-C.)
Housse, R. (2021) · theses.fr (ABES) · doi:10.70675/1c9d985fz6ad4z4956z8cc1zbee25079980f
Object-Based Image Analysis: A Review of Developments and Future Directions of Automated Feature Detection in Landscape Archaeology
Davis, D. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3827105

Flagged first, then by citations

Research works printed but not linked: 10

Uribe, 2011, Las sociedades Formativas de San Pedro de Atacama: Asentamiento, cronología y proceso, Estud. Atacameños, 42, 53
Jackson, 2004, Hallazgo o búsqueda de sitios paleoindios: Problemas de investigación en torno a los primeros poblamientos, Rev. Werken, 5, 9
Jackson, 2005, Urban expansion in northern Chile and its implications for cultural heritage, J. Anthropol. Archaeol., 24, 408
Santoro, 2011, El tránsito Arcaico-Formativo en la Circumpuna y Valles Occidentales del Centro Sur Andino: Hacia los cambios “neolíticos”, Chungara, 43, 487
Uribe, 2004, Tiwanaku en Chile: Nuevas evidencias, nuevos debates, Boletín Mus. Chil. Arte Precolomb., 13, 35
Sonia, A., and Covey, A. (2018). Inca Imperial Colonization in Northern Chile. The Oxford Handbook of the Incas, Oxford University Press.
Hidalgo, J., and Focacci, G. (1986). Historia prehispánica de Arica y el valle de Azapa. Chungara: Revista de Antropología Chilena No. 16/17, actas x Congreso Nacional de Arqueologia Chilena (Octubre 1986), Universidad de Tarapaca. Available online: https://www.jstor.org/stable/27801889.
Chacama, 2012, Transformación del paisaje social en Arica, Norte de Chile: De pescadores arcaicos a agricultores incipientes, Estud. Atacameños, 44, 123
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv.
Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv.

Sorted by citations

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Zhu, L., Zhang, X., Zhao, C., Gao, M., Lyu, H., & Wang, Z. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133476
Deep Learning to Detect Stone Burial Sites from Drone Images in the Southern Foothills of the Tianshan Mountains in Xinjiang, China
Yang, H., Zou, Q., Hu, Q., Wang, S., Zhao, P., Ai, M., & Li, Y. (2026) · Journal of Field Archaeology · doi:10.1080/00934690.2026.2731301

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AI Driven Archaeological Site Mapping
Sonawane, M., More, R., Potdukhe, S. D., Chaudhari, P., & Pol, H. (2026) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem56685
Prospecting of Architectural Features Using LiDAR‐UAV Technology, Deep Neural Networks and Visualization Techniques: A Case Study in Kuélap and Cambolín (NW Peru)
Zabaleta-Santisteban, J. A., López, R. S., Medina-Medina, Á. J., Tuesta-Trauco, K. M., Rivera-Fernandez, A. S., Noriega-Salazar, N. M., Sánchez-Vega, J. A., Silva-Melendez, T. B., Grandez-Alberca, M. A., Silva-López, J. O., et al. (2026) · Archaeological Prospection · doi:10.1002/arp.70033 · Cited by 1
A YOLO-Based Workflow for Detecting and Mapping Archaeological Stone Cairns in Satellite Imagery: A Case Study from Western Ennedi, Chad
Ghaderpour, E., Djetounako, C., Noudjiko, H. M., Rossi, P. P., Rotunno, R., & di Lernia, S. (2026) · Heritage · doi:10.3390/heritage9060237
Towards the use of artificial intelligence deep learning networks for detection of archaeological sites
Karamitrou, A., Sturt, F., Bogiatzis, P., & Beresford-Jones, D. (2022) · Surface Topography: Metrology and Properties · doi:10.1088/2051-672x/ac9492 · Cited by 29

Authors: Castiello, M. E., Landauer, J., & Saintenoy, T.
Affiliations: Institute d’Archéologie et de Sciences de l’Antiquité, University of Lausanne, CH-1015 Lausanne, Switzerland
Landauer Research, 71642 Ludwigsburg, Germany
Consejo Superior de Investigaciones Científicas (ES)
ORCID: Castiello, Maria Elena
Saintenoy, Thibault
Venue: Remote Sensing
Publisher: MDPI AG
Published: 2025-10-21
Volume: 17
Issue: 20
Pages: 3499
ISSN: 2072-4292 (ISSN-L)
DOI: 10.3390/rs17203499
OpenAlex: W4415403352
Cited by: 2
References: 54
Citations by year: 2026: 2
FWCI: 2.99
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Remote Sensing (publishedVersion, cc-by)
DIGITAL.CSIC (Spanish National Research Council (CSIC)) (acceptedVersion, other-oa)
HAL (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
IRIS (submittedVersion)
Topics: Social Sciences › Archeology › Archaeological Research and Protection
Social Sciences › Arts and Humanities › Archeology (arts and humanities) › Archaeology and Cultural Heritage
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Processing and 3D Reconstruction
Keywords: convolutional neural networks, archaeological mapping, Andean highlands, Azapa Valley, roundhouses, corrals, stone structures, satellite imagery, aerial photograph interpretation

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Remote Sensing and AI Coupled Approach for Large-Scale Archaeological Mapping in the Andean Arid Highlands: Case Study in Altos Arica, Chile

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