University of Oulu

Anttiroiko N, Groesz FJ, Ikäheimo J, Kelloniemi A, Nurmi R, Rostad S, Seitsonen O. Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning. Remote Sensing. 2023; 15(7):1799.

Detecting the archaeological traces of tar production kilns in the northern boreal forests based on airborne laser scanning and deep learning

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Author: Anttiroiko, Niko1; Groesz, Floris Jan2; Ikäheimo, Janne3;
Organizations: 1Finnish Heritage Agency, 00510 Helsinki, Finland
2Field/Blom, NO-0283 Oslo, Norway
3Archaeology, Humanities, University of Oulu, 90570 Oulu, Finland
4Archaeology, Humanities, University of Helsinki, 00014 Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 8.4 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2023
Publish Date: 2023-03-30


This paper presents the development and application of a deep learning-based approach for semi-automated detection of tar production kilns using new Finnish high-density Airborne Laser Scanning (ALS) data in the boreal taiga forest zone. The historical significance of tar production, an important livelihood for centuries, has had extensive environmental and ecological impacts, particularly in the thinly inhabited northern and eastern parts of Finland. Despite being one of the most widespread archaeological features in the country, tar kilns have received relatively little attention until recently. The authors employed a Convolutional Neural Networks (CNN) U-Net-based algorithm to detect these features from the ALS data, which proved to be more accurate, faster, and capable of covering systematically larger spatial areas than human actors. It also produces more consistent, replicable, and ethically sustainable results. This semi-automated approach enabled the efficient location of a vast number of previously unknown archaeological features, significantly increasing the number of tar kilns in each study area compared to the previous situation. This has implications also for the cultural resource management in Finland. The authors’ findings have influenced the preparation of the renewal of the Finnish Antiquities Act, raising concerns about the perceived impacts on cultural heritage management and land use sectors due to the projected tenfold increase in archaeological site detection using deep learning algorithms. The use of environmental remote sensing data may provide a means of examining the long-term cultural and ecological impacts of tar production in greater detail. Our pilot studies suggest that artificial intelligence and deep learning techniques have the potential to revolutionize archaeological research and cultural resource management in Finland, offering promising avenues for future exploration.

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Series: Remote sensing
ISSN: 2072-4292
ISSN-E: 2072-4292
ISSN-L: 2072-4292
Volume: 15
Issue: 7
Article number: 1799
DOI: 10.3390/rs15071799
Type of Publication: A1 Journal article – refereed
Field of Science: 615 History and archaeology
Funding: This research was funded by Finnish Ministry of Agriculture and Forestry decision number VN/22710/2020-MMM-3 and Kone Foundation project 202203221.
Copyright information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (