Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents.

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.