Towards Multi-class Forest Floor Analysis

Maximilian Johenneken, Ahmad Drak, Mihir Mulye, Taha Gharaibeh, Alexander Asteroth

Abstract: Climate change induced events such as drought lead to stress in forest trees, causing major catastrophic outbreaks of bark beetle insects. Current efforts to mitigate the infestation is to mass log an affected area. As a result the forest floor of a post-harvest area is cluttered with various objects such as tree logs and twigs. This study is aimed towards exploring basic computer vision methods that make use of shape, elevation and color to detect and segment objects on the forest floor. Such methods have advantages over learning methods due to their simplicity and speed in producing initial usable results, in addition to the low requirement of computational resources and training data. The highest intersection over union result of the multi-class detection and segmentation method is 0.64 for the road class. Such methods prove the feasibility of deploying basic computer vision techniques to acquire fast and reliable results.