An intelligent framework for end‐to‐end rockfall detection

Abstract

Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state‐of‐the‐art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well‐known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above‐mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.

High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

“…Many of these false positives were a result of shadows or other lighting differences between dates being compared. Reliable methods of cluster identification and classification are required for the methods presented in this study to be fully automated and applicable to real-world scenarios where rigorous manual validation is not practical [46,47]. Relatedly, almost half of the validated changes over 5 m 3 corresponded to slope-ravel-type processes where there was no identifiable or coherent rock block that moved.…”

Monitoring the Effects of Slope Hazard Mitigation and Weather on Rockfall along a Colorado Highway Using Terrestrial Laser Scanning

“…The most common approach to solving this problem and extracting only true rockfalls is to impose some basic filtering criteria, such as a minimum number of points and symmetry conditions for the front and back face of the cluster, combined with manual verification [20,29]. Other approaches involve calculating statistics for each cluster, such as the minimum and maximum change values and the lengths of the principal axes, then applying a machinelearning algorithm to classify each cluster as either a true rockfall or non-rockfall [21,36]. Schovanec et al [21] showed that automated cluster filtering enabled a much more realistic estimate of the frequency-magnitude distribution of rockfalls with a classification error rate of less than 20%.…”