Automatic extraction of rockfall source based on terrain analysis map using support vector machine

Disaster prevention inspections, without overlooking the sources of falling rock, are essential for establishing efficient slope management and countermeasures. This study presents an automatic extraction method for rockfall sources, using remote sensing and artificial intelligence technology, to reduce overlooking during the inspection cycle and improve slope management efficiency. Current inspections have some factors that lead to object overlooking, one of them being the use of maps with low expression accuracy. Furthermore, biased criteria for the interpretation of inspection points in a desk study can play a part. Therefore, improving map accuracy and using quantified interpretation methods will improve the current inspection significantly. In such cases, the use of remote sensing technology is an effective measure. The utilization of airborne laser surveying and terrain analysis methods is effective for accurately acquiring the slope surface and topography. Airborne laser surveying is a system that takes measurements with multiple sensors mounted on an airplane, and the measurement data are represented by a collection of multiple points with three-dimensional coordinates. Furthermore, the terrain analysis method, which converts the survey data to two-dimensional raster images, extracts the necessary information, such as the ridge, valley, and elevation, of the slope. In this study, two-dimensional continuous wavelet transforms, used as a terrain analysis method suitable for rockfall extraction, are adopted to create a wavelet analysis map from survey data. Furthermore, to automatically extract the inspection points, the classification technology in artificial intelligence (AI) is applied to the terrain analysis map to extract the rockfall source in a desk study. A support vector machine (SVM) is a type of AI model that classifies based on training data and works by determining the best possible separation between the closest observations belonging to different classes. By applying this classification method, the rockfall source was extracted by performing object detection on the map. First, the entire map was divided into smaller patch images. Next, each patch image is classified as a rockfall source using the trained SVM. Finally, the area corresponding to the patch image, classified as the rockfall source, was drawn on the entire map. In this study, the performance of these integrated systems was verified in an area with a falling rock hazard. In the training process, wavelet analysis maps that reflect inventory data based on past inspection results were used. The extraction performance was evaluated by comparing the verification results with the inventory data and interpretation results based on the map features. Consequently, all learned rockfall source points were extracted, obtaining high-precision readability. Furthermore, the extraction performance of the same tendency inside the inventory range was shown in the range outside the inventory, acquiring a high versatility. Accordingly, we discuss the possibilities and issues for automatic extraction of the desk survey using the proposed method.