2 research outputs found
End-to-End Intelligent Framework for Rockfall Detection
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 or digital cameras.
Multi-temporal 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 expertise 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 poses significant research challenges
and justifies intensive 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
benchmarked and compared applying well-known metrics to be incorporated into
our system. Specifically, we developed statistical and machine learning
techniques and applied them to analyze point cloud data extracted from
Terrestrial Laser Scanner 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 data
suggest that some of the above-mentioned machine learning pipelines can be
utilized to detect rockfall incidents on mountain walls, with experimentally
proven accuracy
Machine Learning-Based Rockfalls Detection with 3D Point Clouds, Example in the Montserrat Massif (Spain)
Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models