Tag Archives: point cloud

Special Issue “Use of LiDAR and 3D point clouds in Geohazards”

We have been invited for edit a special issue at Remote Sensing journal entitled: “Use of LiDAR and 3D point clouds in Geohazards”

Deadline for manuscript submissions: 31 May 2015

SPECIAL ISSUE CONTENTS:

Contributions aiming to use 3D point clouds for investigating natural phenomena (including ground deformation, landslides, floods, earthquakes, volcanoes, soil erosion, etc.) that pose serious risks to human beings or infrastructures will be much appreciated in this special issue. We aim to put together innovative contributions about novel processing techniques and original applications of three-dimensional techniques in Geohazards. Some examples include, but are not limited to:

  • Novel technologies or procedures for dynamic acquisition of 3D point clouds
  • New computational methods related with the monitoring of natural phenomena
  • Semi-automatic extraction of terrain features related with the characterization of geological hazard
  • Integration of very high quality data for improving the modeling of geohazards
  • Recent case studies: innovative analysis and interpretation of Geohazards
  • Use of three-dimensional systems in laboratory scale experiments (micro scale)
  • Improvements in regional mapping derived from high quality 3D data
  • Pioneering initiatives for the creation of 3D databases, web visualization and data sharing
  • Further related topics.

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Yours sincerely,

Prof. Michel Jaboyedoff, Dr. Antonio Abellan, Dr. Marc-Henri Derron (Guest Editors)

More information here: http://www.mdpi.com/journal/remotesensing/special_issues/geohazards

New publication in Computer and Geosciences

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

Riquelme, A., Abellan, A., Tomás, R., Jaboyedoff, M (2014). A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Computers & Geosciences, 68(0), pp.38–52. DOI: 10.1016/j.cageo.2014.03.014

In this paper we present a semi-authomatic methodology for the identification and analysis of flat surfaces outcropping in rocky slopes from 3D point clouds. The method is based on a three steps process: (i) to carry out a nearest neighbour points coplanarity test and to computate the normal vector; (ii) to find the principal orientation families by Kernel Density Estimation; and (iii) to identify and extract clusters of points belonging to the same discontinuity.

Different sources of information – synthetic and 3D scanned data – were employed in this study, being raw source files and obtained results freely provided aiming to a more reproducible research.

Discontinuity extraction
Example of clusters identification. (a) One colour per discontinuity set with all clusters labelled; from (b) to (f) family sets representation using one colour per cluster (Riquelme et al., 2014).