Applying Machine Learning to Detect Historical Remains in Swedish Forestry Using LIDAR Data

Abstract: Historical remains in Swedish forests are at risk of being damaged by heavy machineryduring regular soil preparation, scarification, and regeneration activities. Thereason for this is that the exact locations of these remains are often unknown or theirrecords are inaccurate. Some of the most vulnerable historical remains are the tracesleft after years of charcoal production. In this thesis, we design and implement acomputer vision artificial intelligent model capable of identifying these traces usingtwo accessible visualizations of Light Detection and Ranging (LIDAR) data. Themodel we used was the ResNet34 Convolutional Neural Network pre-trained on theImageNet dataset. The model took advantage of the image segmentation approachand required only a small number of annotations distributed on original images fortraining. During the process of data preparation, the original images were heavilyaugmented, which bolstered the training dataset. Results showed that the model candetect charcoal burners sites and mark them on both types of LIDAR visualizations.Being implemented on modern frameworks and featured with state-of-art machinelearning techniques, the model may reduce the costs of surveys of this type of historicalremains and thereby help save cultural heritage.