ROADSIDE FOREST MODELING USING DASHCAM VIDEOS AND CONVOLUTIONAL NEURAL NETS

Abstract

Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to execute roadside vegetation management practices, at the landscape level, without proper monitoring of roadside forests’ physical structure and health condition. Remote sensing images and LiDAR are widely used to characterize the forest edge; however, the limitation on the temporal and spatial resolution for most of that dataset is a big challenge. Also, there is a need for a ground-level dataset that provides the vertical profile of the forest trees so that we can more accurately characterize the forest structure and health and recommend the optimal management strategies according to the local forest conditions. For the first time, we introduced Dashcam videos as an alternative to the existing aerial remote sensing data sources to characterize the roadside forest condition using the deep learning (DL) convolutional neural net (CNN) algorithms. In this study, we used dashcam videos taken during the leaf-on and leaf-off conditions and various weather conditions along the roadside. We trained a DLCNN model based on the U-Net and YOLO v5 architectures to classify the multilayer vegetation and detect utility poles and tree trunks alongside the road. Our experiment results suggest that a dashcam can be a viable alternative and complementary way to characterize the roadside vegetation and can be used in the management of roadside forests as a cost-effective data acquisition mechanism for utility companies.