  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.
