Detection of hunting pits using airborne laser scanning and deep learning

• Published in: Journal of Field Archaeology
• Main Authors: Lidberg, William, Westphal, Florian, Brax, Christoffer, Sandström, Camilla, Östlund, Lars
• Format: Article in Journal/Newspaper
• Language: English
• Published: UmeÃ¥ universitet, Statsvetenskapliga institutionen 2024
• Subjects: Archaeology; forest history; hunting pits; airborne laser scanning; artificial intelligence; deep learning; machine learning; Forest Science; Skogsvetenskap; Other Social Sciences not elsewhere specified; Övrig annan samhällsvetenskap; Arkeologi; sami
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-228297; https://doi.org/10.1080/00934690.2024.2364428

Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern forest management methods threaten these ancient monuments and cultural remains. In northern Europe, older forests often contain very old traces, such as millennia-old hunting pits and indigenous Sami hearths. Investigations have repeatedly found that forest owners often fail to protect these cultural remains and that many are damaged by forestry operations. Current maps of hunting pits are incomplete, and the locations of known pits have poor spatial accuracy. This study investigated whether hunting pits can be automatically mapped using national airborne laser data and deep learning. The best model correctly mapped 70% of all the hunting pits in the test data with an F1 score of 0.76. This model can be implemented across northern Scandinavia and could have an immediate effect on the protection of cultural remains.