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An intelligent framework for end‐to‐end rockfall detection
- Source :
- Dipòsit Digital de la UB, Universidad de Barcelona
- Publication Year :
- 2021
- Publisher :
- Wiley, 2021.
-
Abstract
- Rockfall detection is a crucial procedure in the field ofgeology, which helps to reduce the associated risks.Currently, geologists identify rockfall events almostmanually utilizing point cloud and imagery data ob-tained from different caption devices such as TerrestrialLaser Scanner (TLS) or digital cameras. Multitemporalcomparison of the point clouds obtained with thesetechniques requires a tedious visual inspection to iden-tify rockfall events which implies inaccuracies that de-pend on several factors such as human expertize and thesensibility of the sensors. This paper addresses this issueand provides an intelligent framework for rockfall eventdetection for any individual working in the intersectionof the geology domain and decision support systems.The development of such an analysis framework pre-sents major research challenges and justifies exhaustiveexperimental analysis. In particular, we propose an in-telligent system that utilizes multiple machine learningalgorithms to detect rockfall clusters of point cloud data.Due to the extremely imbalanced nature of the problem,aplethoraofstateoftheart resampling techniques ac-companied by multiple models and feature selectionprocedures are being investigated. Various machine learning pipeline combinations have been examinedand benchmarked applying wellknown metrics to beincorporated into our system. Specifically, we developedmachine learning techniques and applied them to ana-lyze point cloud data extracted from TLS in two distinctcase studies, involving different geological contexts: thebasaltic cliff of Castellfollit de la Roca and the con-glomerate Montserrat Massif, both located in Spain. Ourexperimental results indicate that some of the abovementioned machine learning pipelines can be utilized todetect rockfall incidents on mountain walls, with ex-perimentally validated accuracy.
- Subjects :
- geography
geography.geographical_feature_category
010504 meteorology & atmospheric sciences
Computer science
Real-time computing
Intelligent decision support system
02 engineering and technology
01 natural sciences
Theoretical Computer Science
Human-Computer Interaction
Rockfall
End-to-end principle
Artificial Intelligence
Esllavissades
Fotogrametria
Photogrammetry
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Software
Landslides
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Dipòsit Digital de la UB, Universidad de Barcelona
- Accession number :
- edsair.doi.dedup.....9186f1def990babba39f2061fa6799b1