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

Authors :
Iban Berganzo-Besga
Hector A. Orengo
Felipe Lumbreras
Miguel Carrero-Pazos
João Fonte
Benito Vilas-Estévez
Source :
Remote Sensing, Vol 13, Iss 20, p 4181 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
13204181 and 20724292
Volume :
13
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.99bc46c61b5c47e6bec2b734d914c312
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13204181