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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. [ABSTRACT FROM AUTHOR]

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes. [ABSTRACT FROM AUTHOR]