A new approach for semi-automatic rock mass joints recognition from 3D point clouds

By A. Riquelme, A. Abellán, R. Tomás, M. Jaboyedoff. Computational Geosciences, 2014

395 citations recorded by the source corpus.

Subfields: Environmental Engineering, Geology, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/a85f8da01cc67b8500b021a6684055e096340516

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Marco Scaioni
2. Gregory C. Beroza
3. Valentina Melillo
4. Laura Longoni
5. Monica Papini

In Geology

1. Gregory C. Beroza

In Management, Monitoring, Policy and Law

1. Marco Scaioni
2. Valentina Melillo
3. Laura Longoni
4. Monica Papini

Citations in the corpus

• Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives (2014, methodology): “Recently, a new approach for semi-automatic calculation of the orientations and position of rock mass discontinuities from 3D ALS data has been presented [205].”
• Machine learning for data-driven discovery in solid Earth geoscience (2019, background): “Data sources such as light detection and ranging (LiDAR) point clouds ( 134 ), distributed acoustic sensing with fiber optic cables ( 135 – 137 ), and crowd-sourced data from smartphones ( 17 ), social media ( 138 , 139 ), web traffic ( 140 ), and microelectromechanical systems (MEMS)…”