UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Berganzo-Besga, I; Orengo, HA; Lumbreras, F; Carrero-Pazos, M; Fonte, J; Vilas-Estévez, B; (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 (20) p. 4181. 10.3390/rs13204181. Green open access

[thumbnail of Berganzoetal.2021.pdf]
Preview
Text
Berganzoetal.2021.pdf - Accepted Version

Download (3MB) | Preview

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.

Type: Article
Title: Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm Detects Near 10k Archaeological Tumuli in North-Western Iberia
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/rs13204181
Publisher version: https://doi.org/10.3390/rs13204181
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: tumuli; mounds; archaeology; deep learning; machine learning; Sentinel-2; Google Colaboratory; Google Earth
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology > Institute of Archaeology Gordon Square
URI: https://discovery.ucl.ac.uk/id/eprint/10136958
Downloads since deposit
20Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item