Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning
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 li...
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2023
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ftdoajarticles:oai:doaj.org/article:3b5de5040b0342f0afdd6c2e3632c6cb 2023-06-06T11:59:48+02:00 Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning Niko Anttiroiko Floris Jan Groesz Janne Ikäheimo Aleksi Kelloniemi Risto Nurmi Stian Rostad Oula Seitsonen 2023-03-01T00:00:00Z https://doi.org/10.3390/rs15071799 https://doaj.org/article/3b5de5040b0342f0afdd6c2e3632c6cb EN eng MDPI AG https://www.mdpi.com/2072-4292/15/7/1799 https://doaj.org/toc/2072-4292 doi:10.3390/rs15071799 2072-4292 https://doaj.org/article/3b5de5040b0342f0afdd6c2e3632c6cb Remote Sensing, Vol 15, Iss 1799, p 1799 (2023) airborne laser scanning archaeology feature detection deep learning tar production boreal forest Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15071799 2023-04-16T00:33:18Z 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. Article in Journal/Newspaper taiga Directory of Open Access Journals: DOAJ Articles Remote Sensing 15 7 1799 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
airborne laser scanning archaeology feature detection deep learning tar production boreal forest Science Q |
spellingShingle |
airborne laser scanning archaeology feature detection deep learning tar production boreal forest Science Q Niko Anttiroiko Floris Jan Groesz Janne Ikäheimo Aleksi Kelloniemi Risto Nurmi Stian Rostad Oula Seitsonen Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
topic_facet |
airborne laser scanning archaeology feature detection deep learning tar production boreal forest Science Q |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Niko Anttiroiko Floris Jan Groesz Janne Ikäheimo Aleksi Kelloniemi Risto Nurmi Stian Rostad Oula Seitsonen |
author_facet |
Niko Anttiroiko Floris Jan Groesz Janne Ikäheimo Aleksi Kelloniemi Risto Nurmi Stian Rostad Oula Seitsonen |
author_sort |
Niko Anttiroiko |
title |
Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
title_short |
Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
title_full |
Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
title_fullStr |
Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
title_full_unstemmed |
Detecting the Archaeological Traces of Tar Production Kilns in the Northern Boreal Forests Based on Airborne Laser Scanning and Deep Learning |
title_sort |
detecting the archaeological traces of tar production kilns in the northern boreal forests based on airborne laser scanning and deep learning |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15071799 https://doaj.org/article/3b5de5040b0342f0afdd6c2e3632c6cb |
genre |
taiga |
genre_facet |
taiga |
op_source |
Remote Sensing, Vol 15, Iss 1799, p 1799 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/7/1799 https://doaj.org/toc/2072-4292 doi:10.3390/rs15071799 2072-4292 https://doaj.org/article/3b5de5040b0342f0afdd6c2e3632c6cb |
op_doi |
https://doi.org/10.3390/rs15071799 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
7 |
container_start_page |
1799 |
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