Deep Learning-Based Automated Slope Condition Assessment using Street View and Satellite Imagery

Abstract: Manual inspection of highway slopes is labor-intensive and time-consuming, making it difficult to monitor and assess slope conditions consistently. With the increased availability of street view and satellite imagery and advancements in deep learning techniques, automated systems for slope condition assessment offer an efficient and scalable solution. This research focuses on developing an automated deep learning-based approach using street view and satellite imagery to detect and classify early slope failure indicators such as erosion, vegetation loss, cracks, water runoff, and landslide events. The primary objective is to leverage a convolutional neural network (CNN) architecture, enhanced with attention mechanisms and multi-scale feature extraction techniques, to accurately detect early slope failure indicators such as erosion, cracks, and vegetation loss. A custom dataset of slope images and corresponding masks is used for training and validation, and performance metrics, including accuracy, precision, recall, F1 score, and Mean Intersection over Union (mIoU) are computed during model evaluation. The results demonstrate the effectiveness of this approach in detecting slope failure indicators, with the model achieving high accuracy and a consistent reduction in loss over the training period. This approach provides an automated, efficient, and scalable solution to monitor slope conditions in transportation infrastructure. Detecting and classifying early signs of slope failure like erosion, cracks, and vegetation changes helps transportation agencies ensure safety, optimize maintenance resources, and prevent infrastructure damage.