Next Article in Journal
An Overview of the Special Issue on Plant Phenotyping for Disease Detection
Previous Article in Journal
Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution
Article

Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia

1
Landscape Archaeology Research Group, Catalan Institute of Classical Archaeology, Pl. Rovellat s/n, 43003 Tarragona, Spain
2
Computer Vision Center, Computer Science Deptartment, Universitat Autònoma de Barcelona, Edifici O, Campus UAB, 08193 Bellaterra, Spain
3
Institute of Archaeology, University College London, 31-34 Gordon Square, London WC1H 0PY, UK
4
Department of Archaeology, University of Exeter, Laver Building, North Park Road, Exeter EX4 4QE, UK
5
Grupo de Estudos de Arqueoloxía, Antigüidade e Territorio, Facultade de Historia, University of Vigo, As Lagoas, s/n, 32004 Ourense, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Timo Balz
Remote Sens. 2021, 13(20), 4181; https://doi.org/10.3390/rs13204181
Received: 21 September 2021 / Revised: 14 October 2021 / Accepted: 16 October 2021 / Published: 19 October 2021
This paper presents an algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available. View Full-Text
Keywords: tumuli; mounds; archaeology; deep learning; machine learning; Sentinel-2; Google Colaboratory; Google Earth Engine tumuli; mounds; archaeology; deep learning; machine learning; Sentinel-2; Google Colaboratory; Google Earth Engine
Show Figures

Graphical abstract

MDPI and ACS Style

Berganzo-Besga, I.; Orengo, H.A.; Lumbreras, F.; Carrero-Pazos, M.; Fonte, J.; Vilas-Estévez, B. Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia. Remote Sens. 2021, 13, 4181. https://doi.org/10.3390/rs13204181

AMA Style

Berganzo-Besga I, Orengo HA, Lumbreras F, Carrero-Pazos M, Fonte J, Vilas-Estévez B. Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia. Remote Sensing. 2021; 13(20):4181. https://doi.org/10.3390/rs13204181

Chicago/Turabian Style

Berganzo-Besga, Iban, Hector A. Orengo, Felipe Lumbreras, Miguel Carrero-Pazos, João Fonte, and Benito Vilas-Estévez. 2021. "Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia" Remote Sensing 13, no. 20: 4181. https://doi.org/10.3390/rs13204181

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop