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Title: Machine Learning-Based Rockfalls Detection with 3D Point Clouds, Example in the Montserrat Massif (Spain)
Author: Blanco Núñez, Laura
García Sellés, David
Guinau Sellés, Marta
Zoumpekas, Thanasis
Puig Puig, Anna
Salamó Llorente, Maria
Gratacós Torrà, Òscar
Muñoz, J. A.
Janeras Casanova, Marc
Pedraza, Oriol
Keywords: Esllavissades
Moviments de massa
Vigilància electrònica
Montserrat (Catalunya : Massís)
Electronic surveillance
Montserrat Mountain (Catalonia)
Issue Date: 1-Sep-2022
Publisher: MDPI
Abstract: 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.
Note: Reproducció del document publicat a:
It is part of: Remote Sensing, 2022, vol. 14, num. 17, p. 4306
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ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Dinàmica de la Terra i l'Oceà)

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