Remote Sensing and Geovisualization of Rock Slopes and Landslides

Abstract

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists.

Full text

Citation: Donati, D.; Stead, D.; Onsel,
E.; Mysiorek, J.; Chang, O. Remote
Sensing and Geovisualization of Rock
Slopes and Landslides. Remote Sens.
2023,15, 3702. https://doi.org/
10.3390/rs15153702
Academic Editor: Peter V Gorsevski
Received: 5 June 2023
Revised: 16 July 2023
Accepted: 17 July 2023
Published: 25 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Remote Sensing and Geovisualization of Rock Slopes
and Landslides
Davide Donati 1, * , Doug Stead 2, Emre Onsel 3, Jesse Mysiorek 4and Omar Chang 2
1Department of Civil, Chemical, Environmental, and Material Engineering, Alma Mater
Studiorum—University of Bologna, 40126 Bologna, Italy
2Department of Earth Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
doug_stead@sfu.ca (D.S.); omarc@sfu.ca (O.C.)
3SRK Consulting, Vancouver, BC V6C 1S9, Canada; eonsel@srk.com
4Clifton Engineering Group Inc., Calgary, AB T2C 5C2, Canada; jesse_mysiorek@clifton.ca
*Correspondence: davide.donati17@unibo.it
Abstract:
Over the past two decades, advances in remote sensing methods and technology have
enabled larger and more sophisticated datasets to be collected. Due to these advances, the need
to effectively and efficiently communicate and visualize data is becoming increasingly important.
We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very
promising results, allowing the visualization of complex datasets with unprecedented levels of
detail and user experience. However, as of today, such visualization techniques have been largely
used for communication purposes, and limited applications have been developed to allow for data
processing and collection, particularly within the engineering–geology field. In this paper, we
demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote
sensing data but also for the collection and analysis of geological data. In this paper, we present
a conceptual workflow showing the approach used for the processing of remote sensing datasets
and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer
applications built in-house to visualize datasets and numerical modelling results, and to perform
rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important
limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected
technological advances and cost reduction will ensure this technology forms a standard mapping
and data analysis tool for future engineers and geoscientists.
Keywords:
mixed reality; virtual reality; multi-sensor datasets; rock mass characterization; rock
core logging
1. Introduction
The analysis of the stability of rock slopes requires a careful characterization of the
rock mass be performed. Intact rock parameters, such as uniaxial compressive and tensile
strength, and characteristics of discontinuities, including orientation, persistence, and
roughness, contribute to defining the quality of rock masses as well their deformability
and strength [
1
,
2
]. Typically, traditional field techniques can be employed to systematically
collect rock mass and discontinuity data [
3
,
4
]. However, local conditions, such as the
activity of slopes, dense vegetation, and unstable terrain, can pose significant challenges,
limiting or even preventing in situ data collection due to safety or accessibility concerns.
The development, particularly over the past twenty years, of ground-based and
airborne remote sensing (RS) techniques [
5
] has the potential to allow these challenges to
be addressed. RS methods provide a means to collect geological and structural data across
large areas in a relatively short time and are therefore routinely used in rock mass and
terrain characterization at various scales [6–9] as well as for monitoring purposes [10–16].
Remote Sens. 2023,15, 3702. https://doi.org/10.3390/rs15153702 https://www.mdpi.com/journal/remotesensing

Remote Sens. 2023,15, 3702 2 of 22
Terrestrial and airborne laser scanning (TLS and ALS, respectively, [
17
]) and digi-
tal photogrammetric methods, such as Structure-from-Motion (SfM, [
18
]), allow for the
construction of three-dimensional models of rock slopes and outcrops, and they are the
most widely employed methods for rock mass characterization. In particular, compared to
traditional digital photogrammetric techniques, SfM has the advantage of not requiring
camera calibration parameters to be known a priori [
5
], providing greater flexibility in
terms of survey planning and type of digital camera [19].
Three-dimensional models, particularly those derived from TLS and SfM, can be
used to identify and map discontinuity planes and traces as well as to derive data, such as
orientation, persistence, and spacing [
5
,
20
]. Conversely, ALS datasets are particularly suited
for terrain analysis and are routinely employed to perform structural analyses through
slope-scale and regional-scale lineament mapping (e.g., [21]).
Infrared methods have also recently been introduced for the analysis of rock slopes. In-
frared thermography (IRT) involves the analysis of the infrared radiation of an object, which
is a function of its temperature [
22
]. IRT proved effective in the identification and mapping
of groundwater seepage [
23
], open cracks [
24
,
25
], near-surface intact rock bridges [
9
], and in
the analysis of thermal behavior (e.g., cooling index) and thermal anomalies of rock slopes
and rock masses [
26
–
28
]. While IRT investigates the global electromagnetic emission in the
infrared band (1 mm–700 nm range), hyperspectral imagery (HSI) is capable of capturing
the electromagnetic radiation of objects across several (typically, several hundred) discrete
wavelengths [
29
]. Since the electromagnetic radiation in the short-wave infrared range
(SWIR, 1.1–3
µ
m) is diagnostic of the chemical and mineral composition of the material,
HSI has been largely employed for airborne application (e.g., [
30
]), particularly in the
mining exploration field (e.g., [
31
]). More recently, ground-based applications have been
developed, allowing for the analysis of lithological variations along steep slopes [
32
,
33
]
and along rock cores [34].
RS methods provide spatial coverage for geological data that is significantly greater
than that available through traditional field methods, which are limited by terrain, height,
and site accessibility. They also can provide a wealth of geological information, ranging
from structural and geomorphic to lithological and mineralogical data, requiring effective
visualization methods to enhance the interpretation, communication, and sharing of these
extensive datasets. In this sense, advanced mixed and virtual reality methods can be
employed to optimize combined visualization and interpretation of advanced datasets.
Conventional reality and complete virtual immersion are located at the far ends of the
“reality-virtuality (RV) continuum” [35] (Figure 1).
Remote Sens. 2023, 15, x FOR PEER REVIEW 2 of 22
large areas in a relatively short time and are therefore routinely used in rock mass and
terrain characterization at various scales [6–9] as well as for monitoring purposes [10–16].
Terrestrial and airborne laser scanning (TLS and ALS, respectively, [17]) and digital
photogrammetric methods, such as Structure-from-Motion (SfM, [18]), allow for the
construction of three-dimensional models of rock slopes and outcrops, and they are the
most widely employed methods for rock mass characterization. In particular, compared
to traditional digital photogrammetric techniques, SfM has the advantage of not requiring
camera calibration parameters to be known a priori [5], providing greater flexibility in
terms of survey planning and type of digital camera [19].
Three-dimensional models, particularly those derived from TLS and SfM, can be
used to identify and map discontinuity planes and traces as well as to derive data, such
as orientation, persistence, and spacing [5,20]. Conversely, ALS datasets are particularly
suited for terrain analysis and are routinely employed to perform structural analyses
through slope-scale and regional-scale lineament mapping (e.g., [21]).
Infrared methods have also recently been introduced for the analysis of rock slopes.
Infrared thermography (IRT) involves the analysis of the infrared radiation of an object,
which is a function of its temperature [22]. IRT proved effective in the identification and
mapping of groundwater seepage [23], open cracks [24,25], near-surface intact rock
bridges [9], and in the analysis of thermal behavior (e.g., cooling index) and thermal
anomalies of rock slopes and rock masses [26–28]. While IRT investigates the global
electromagnetic emission in the infrared band (1 mm–700 nm range), hyperspectral
imagery (HSI) is capable of capturing the electromagnetic radiation of objects across
several (typically, several hundred) discrete wavelengths [29]. Since the electromagnetic
radiation in the short-wave infrared range (SWIR, 1.1–3 µm) is diagnostic of the chemical
and mineral composition of the material, HSI has been largely employed for airborne
application (e.g., [30]), particularly in the mining exploration field (e.g., [31]). More
recently, ground-based applications have been developed, allowing for the analysis of
lithological variations along steep slopes [32,33] and along rock cores [34].
RS methods provide spatial coverage for geological data that is significantly greater
than that available through traditional field methods, which are limited by terrain, height,
and site accessibility. They also can provide a wealth of geological information, ranging
from structural and geomorphic to lithological and mineralogical data, requiring effective
visualization methods to enhance the interpretation, communication, and sharing of these
extensive datasets. In this sense, advanced mixed and virtual reality methods can be
employed to optimize combined visualization and interpretation of advanced datasets.
Conventional reality and complete virtual immersion are located at the far ends of the
“reality-virtuality (RV) continuum” [35] (Figure 1).
Figure 1. The Reality-Virtuality (RV) continuum (modified from [35]).
In particular, the virtual reality (VR) entails the complete overlay of a real
environment by an interactive computer-generated environment that stimulates sensory
responses (i.e., vision and sound), generally achieved using VR devices (e.g., VR goggles,
headsets). Within the virtuality continuum, a progressive transition exists between the
Figure 1. The Reality-Virtuality (RV) continuum (modified from [35]).
In particular, the virtual reality (VR) entails the complete overlay of a real environ-
ment by an interactive computer-generated environment that stimulates sensory responses
(i.e., vision and sound), generally achieved using VR devices (e.g., VR goggles, head-
sets). Within the virtuality continuum, a progressive transition exists between the two
“extreme” environments, entailing the presence of various combinations between reality
and virtuality. Augmented Reality (AR) involves the overlay of datasets within the user’s
field-of-view. Conversely, Augmented Virtuality (AV) involves the emplacement of real

Remote Sens. 2023,15, 3702 3 of 22
objects into virtual environments. Mixed Reality involves the entire RV continuum, except
for the real environment and VR (i.e., the end members), and involves the coexistence
of real and virtual elements within the user’s field-of-view. AR, AV, and VR together
form the Extended Reality (XR). Over the past decade, the application of MR and VR
methods has increased significantly across various fields, including automotive [
36
], health
sciences [
37
], production and manufacturing [
38
], and others. In the field of engineering
geology, mixed and virtual reality have been introduced and applied, particularly for
communication and remote collaboration purposes [
39
]. However, recent work has shown
the potential application of these visualization methods for geotechnical data collection
and processing [40,41].
In this paper, we demonstrate the challenges linked to the effective visualization of
multiple RS datasets as well as the potential advantages in the application of MR and VR
methods for the immersive visualization of integrated multi-sensor RS datasets of rock
slopes. To do so, we describe the geological and geomechanical analyses conducted at
various sites and various scales, highlighting, for each case, the importance of using a
multi-sensor RS approach and the main challenges of the analysis, with an emphasis on
those linked to dataset visualization. We present various methods and approaches for
the processing and visualization of multi-sensor RS data and describe workflows for the
collection and integration of RS datasets for three-dimensional visualization of rock slopes,
both with traditional methods and within MR/VR systems. We describe MR/VR software
applications developed for the advanced analysis and processing of RS datasets, including
applications for performing virtual core logging, the three-dimensional visualization of
rockfall simulations, and a mapping suite, created in collaboration with SRK Consulting
®
(Vancouver, BC, Canada), that allows seamless in situ rock mass characterization through
discontinuity mapping and annotation.
2. Methods
2.1. Remote Sensing Equipment and Software
Data collection is performed using a variety of RS techniques and equipment, including
laser scanning, digital photogrammetry, and infrared methods. Laser scanning data is
collected using a Riegl VZ-4000 (Riegl, Horn, Austria) terrestrial laser scanner, TLS, with
a maximum operating range of 4 km, and incorporating a 5 MPixel digital camera (to
color the point cloud), a compass, a GPS, and an inertial measurement unit (IMU) to
preliminarily register the point clouds in a three-dimensional space. Initial processing
of the point clouds is performed using the proprietary software RiSCAN Pro 2.6 [
42
].
High-resolution photographs (HRP) are collected for texturing 3D models and to perform
Structure-from-Motion photogrammetry using the software Metashape 1.5 [
43
]. Various
DSLR cameras are employed in this study, including a 50 MPixel Canon EOS 5Ds-R,
a 23 MPixel Canon EOS 5D Mark IV, and an 18 MPixel Canon EOS 7D equipped with
an f= 200 mm prime lens or a f= 100–400 telephoto lens. At one of the sites, the Jure
landslide in Nepal, a DJI Mavic Pro quadcopter, with a 12.7 MPixel camera, was used to
collect aerial photographs. IRT datasets are collected using a FLIR SC7000 thermal camera,
equipped with a f= 50 mm or, in some cases, a f= 100 mm focal lens. The SC7000 outputs
raster images with a resolution of 0.41 MPixel (640
×
480), in which each pixel is associated
with a temperature value, automatically computed by the instrument. Post-processing of
thermal imagery and export in other file formats (e.g., jpg, tiff) are conducted using the
software ResearchIR 4.4 [
44
]. HSI imagery is collected using a Specim SWIR3 hyperspectral
scanner, which implements a 384-pixel push-broom sensor mounted on a rotating head.
HSI datasets are processed using the software ENVI 5.5 [45] and following the processing
workflow described in [32].

Remote Sens. 2023,15, 3702 4 of 22
2.2. MR/VR Hardware
The MR system used for this paper includes both the first- and second-generation
Microsoft HoloLens (HL) headsets. The first-generation HoloLens is a stand-alone MR
system, characterized by an Intel Atom CPU, 2 GB RAM, 64 GB storage, running on a
Windows Mixed Reality operating system. The HoloLens 2 is equipped with 4 GB RAM,
Qualcomm Snapdragon 850 SoC. Overall, the second-generation HL provides an improved
holographic density, a wider field of view compared to its predecessor (52
◦
vs. 30
◦
), eye
tracking capability, and improved ergonomics and finger tracking. HL is characterized
by lower computing power compared with VR systems, which can handle larger datasets,
but must be tethered to an external workstation equipped with a compatible GPU. The VR
system used in this paper is an HP Reverb Virtual Reality Headset, powered by an external,
dedicated workstation, featuring an i7 CPU, two Nvidia 1080 GPU, and 128 GB RAM.
2.3. Workflow for Data Collection, Geovisualization, and Processing
Both traditional and innovative visualization methods for combined RS datasets are
demonstrated in this paper. In all cases, three-dimensional datasets are used as a base on
which multiple textures, constituted by rasters from multi-sensor RS datasets, are displayed
(Figure 2). The workflow proposed in [
46
] is used to perform a bundle adjustment process,
exploiting natural points observed on both the three-dimensional and two-dimensional
raster datasets. The workflow takes advantage of the GPL (general public license) software
GNU Octave 8.1 [
47
] and MeshLab 2022.02 [
48
], and allows high-resolution, multi-sensor
textures to be created and registered onto meshes derived from three-dimensional datasets
(e.g., TLS, SfM). Regardless of the sensor employed, a critical step was the export of the
two-dimensional dataset to be exported using a standard format, such as JPEG or TIFF.
Three-dimensional models are then used to digitally characterize the rock mass and
perform discontinuity mapping, using the software CloudCompare 2.12 [
49
]. Point clouds
are used to perform the mapping and compute discontinuity orientation and persistence,
whereas high resolution textures are used as supporting datasets (e.g., to resolve the trace of
discontinuities and distinguish between discontinuity planes and brittle fracture surfaces).
Where available, two- and three-dimensional, multi-temporal datasets are used to perform
change detection analyses in order to investigate and monitor the morphologic evolution
of slopes and other objects [
7
,
50
,
51
]. Three-dimensional meshes are simplified (e.g., the
number of elements reduced) before undertaking numerical modelling analyses in order
to avoid increased runtime due to excessive model geometry detail. Depending on the
scale and size of the model, mesh simplification is performed prior to visualization and
processing through MR or VR systems.
Computer applications for the visualization of RS data in MR and VR systems are
developed using the game engine Unity and its built-in object-oriented programing lan-
guage C# [
52
]. However, prior to bringing any model into Unity for MR and VR application
implementation, the mesh of the model must undergo several preprocessing phases, includ-
ing mesh simplification and reconstruction through software such as CloudCompare 2.12,
MeshLab 2022.02, and/or Blender 2.93 LTS [
53
]. Optimizing the mesh is crucial to avoid
significant reductions in frame rate (potentially causing the user to experience motion
sickness), increased processing times, and system crashes. If the model remains (or is
required to remain) too complex to be visualized using HL, it is reproduced through a VR
application, which is only limited by the computational power of the workstation to which
the system is tethered.

Remote Sens. 2023,15, 3702 5 of 22
Remote Sens. 2023, 15, x FOR PEER REVIEW 5 of 22
Figure 2. Workflow for the collection, processing, and geovisualization of RS datasets. Boxes with
thick outline indicate three-dimensional datasets.
3. Results
In this section, the RS data collection and processing performed at various rock slopes
and landslide sites is described. The analyses conducted at Yak Peak, British Colombia
and Mt Kidd, Alberta in Canada, and Jure landslide, in Nepal, are described. At the Yak
Peak, geological and structural analyses, including rock mass and brile fracture
characterization, were conducted through a traditional visualization approach. RS
datasets collected at Mt Kidd, conversely, were processed and visualized using an MR
approach. At the Jure landslide site, field and RS analyses were conducted. Multi-scale
datasets were then visualized and processed using both traditional displays and an MR
headset. Numerical modelling was also performed and visualized using an MR approach.
3.1. Yak Peak (British Columbia, Canada)
Yak Peak is a rock slope located in the Coquihalla Summit Recreation Area, along the
Coquihalla highway, 30 km north of the municipality of Hope (BC, Canada). This 800 m
high rock slope is formed by granodioritic intrusive rocks of the Needle Peak Pluton that
crystallized during the Eocene era [54,55].
The investigated slope dips towards the south (195°) at an angle varying from 35° at
the base of the slope to 55° in the upper part. The slope is intersected by several geological
Figure 2.
Workflow for the collection, processing, and geovisualization of RS datasets. Boxes with
thick outline indicate three-dimensional datasets.
3. Results
In this section, the RS data collection and processing performed at various rock slopes
and landslide sites is described. The analyses conducted at Yak Peak, British Colombia
and Mt Kidd, Alberta in Canada, and Jure landslide, in Nepal, are described. At the
Yak Peak, geological and structural analyses, including rock mass and brittle fracture
characterization, were conducted through a traditional visualization approach. RS datasets
collected at Mt Kidd, conversely, were processed and visualized using an MR approach.
At the Jure landslide site, field and RS analyses were conducted. Multi-scale datasets
were then visualized and processed using both traditional displays and an MR headset.
Numerical modelling was also performed and visualized using an MR approach.
3.1. Yak Peak (British Columbia, Canada)
Yak Peak is a rock slope located in the Coquihalla Summit Recreation Area, along the
Coquihalla highway, 30 km north of the municipality of Hope (BC, Canada). This 800 m
high rock slope is formed by granodioritic intrusive rocks of the Needle Peak Pluton that
crystallized during the Eocene era [54,55].
The investigated slope dips towards the south (195
◦
) at an angle varying from 35
◦
at
the base of the slope to 55
◦
in the upper part. The slope is intersected by several geological

Remote Sens. 2023,15, 3702 6 of 22
structures (i.e., faults) that dip predominantly towards the northwest (Figure 3a). The rock
mass forming the slope is massive, but rock mass quality decreases near the north-west
dipping faults, indicating the presence of 10 to 30 m wide damage zones surrounding these
geological structures (Figure 3a).
Remote Sens. 2023, 15, x FOR PEER REVIEW 6 of 22
structures (i.e., faults) that dip predominantly towards the northwest (Figure 3a). The rock
mass forming the slope is massive, but rock mass quality decreases near the north-west
dipping faults, indicating the presence of 10 to 30 m wide damage zones surrounding
these geological structures (Figure 3a).
(a)
(b)
Figure 3. (a) 2022 satellite image of the Yak Peak (from Google Earth). The location from which the
remote sensing datasets were collected is indicated. In the inset, the red star shows the location of
the site in British Columbia (Canada). (b) Panoramic image of the slope. Dashed, red lines mark the
NW dipping geological structures that intersect the slope. The inset shows a detail of the damage
zone surrounding these geological structures.
Remote Sensing Analysis
The focus of the analysis conducted at the Yak Peak included (a) a preliminary
discontinuity mapping, and (b) the analysis of brile fracture features generated by the
propagation of exfoliation joints at the surface of the slope.
RS datasets collected at this site include TLS, high resolution imagery, IRT, and HSI.
All the sensors were located at the base of the slope on the opposite side of the highway,
at a distance ranging from 900 m (at the base of the slope) to 1900 m (at the crest).
Discontinuity mapping was conducted using the TLS point cloud. Three
discontinuity sets were identified: the exfoliation joints, E, parallel to the slope, with
orientation 36°/192° (in the paper, discontinuity orientation is reported in dip/dip
direction); J1 (49°/298°); and J2 (42°/041°). In particular, exfoliation joints were noted to act
as a potential basal release surface for tabular blocks, whereas J1 and J2 act as rear release
surfaces (Figure 4a).
Figure 3.
(
a
) 2022 satellite image of the Yak Peak (from Google Earth). The location from which the
remote sensing datasets were collected is indicated. In the inset, the red star shows the location of the
site in British Columbia (Canada). (
b
) Panoramic image of the slope. Dashed, red lines mark the NW
dipping geological structures that intersect the slope. The inset shows a detail of the damage zone
surrounding these geological structures.
Remote Sensing Analysis
The focus of the analysis conducted at the Yak Peak included (a) a preliminary disconti-
nuity mapping, and (b) the analysis of brittle fracture features generated by the propagation
of exfoliation joints at the surface of the slope.
RS datasets collected at this site include TLS, high resolution imagery, IRT, and HSI.
All the sensors were located at the base of the slope on the opposite side of the highway, at
a distance ranging from 900 m (at the base of the slope) to 1900 m (at the crest).
Discontinuity mapping was conducted using the TLS point cloud. Three discontinuity
sets were identified: the exfoliation joints, E, parallel to the slope, with orientation 36
◦
/192
◦
(in the paper, discontinuity orientation is reported in dip/dip direction); J1 (49
◦
/298
◦
); and
J2 (42
◦
/041
◦
). In particular, exfoliation joints were noted to act as a potential basal release
surface for tabular blocks, whereas J1 and J2 act as rear release surfaces (Figure 4a).

Remote Sens. 2023,15, 3702 7 of 22
Remote Sens. 2023, 15, x FOR PEER REVIEW 7 of 22
(a)
(b)
(c)
(d)
Figure 4. Overview of the structural and damage analyses at the Yak Peak: (a) Summary of the
results of the discontinuity mapping performed on the TLS dataset (discontinuity sets marked by
dashed lines with different colors). Note the tabular shape of the blocks generated by the intersection
of the various discontinuity sets (highlighted in red). In the inset, the stereonet (equal area, lower
hemisphere) is shown. (b) Plume features observed along the surface of exfoliation joints (marked
by red lines). Black arrows indicate the expansion direction. (c) Example of a “partial arch” observed
along the rock slope (marked by the dashed red line). (d) Rock bridges (RB) at various stages of
failure. In the inset, a damaged RB: note the en-echelon fracture that formed along its length.
The high-resolution photographs allowed brile fracture features to be highlighted
across the slope. In particular, “plume/feather” features were noted along the surface of
exfoliation joints (Figure 4b), whereas “partial arches” formed the steps that are visible
across the slope (Figure 4c). Plumes and partial arches are both features that typically form
as a result of sudden, brile propagation of fractures in rock [56]. However, it is
challenging to infer the time of formation of such features and to thus evaluate whether
they are the result of the progressive cool down of the pluton after its emplacement or,
alternatively, the stress relief following the exhumation of the plutonic rocks (e.g., [57]).
Various rock bridges at varying stages of damage were also identified: intact (no
fracturing evidence observed along the rock bridge), damaged (single or cluster of cracks
observed within the intact rock of the rock bridge), and failed (presence of a fully
persistent fracture across the originally intact rock) (Figure 4d).
IRT dataset was collected with the FLIR SC7000 coupled with the f = 50 mm focal lens.
Thermal data showed limited or no seepage across the slope, likely due to the absence of
significant rainfall events in the days preceding the survey (28 August 2017). A
significantly weer area was noted, however, at an outcropping area in the lower part of
Figure 4.
Overview of the structural and damage analyses at the Yak Peak: (
a
) Summary of the
results of the discontinuity mapping performed on the TLS dataset (discontinuity sets marked by
dashed lines with different colors). Note the tabular shape of the blocks generated by the intersection
of the various discontinuity sets (highlighted in red). In the inset, the stereonet (equal area, lower
hemisphere) is shown. (
b
) Plume features observed along the surface of exfoliation joints (marked by
red lines). Black arrows indicate the expansion direction. (
c
) Example of a “partial arch” observed
along the rock slope (marked by the dashed red line). (
d
) Rock bridges (RB) at various stages of
failure. In the inset, a damaged RB: note the en-echelon fracture that formed along its length.
The high-resolution photographs allowed brittle fracture features to be highlighted
across the slope. In particular, “plume/feather” features were noted along the surface of
exfoliation joints (Figure 4b), whereas “partial arches” formed the steps that are visible
across the slope (Figure 4c). Plumes and partial arches are both features that typically form
as a result of sudden, brittle propagation of fractures in rock [
56
]. However, it is challenging
to infer the time of formation of such features and to thus evaluate whether they are the
result of the progressive cool down of the pluton after its emplacement or, alternatively, the
stress relief following the exhumation of the plutonic rocks (e.g., [57]).
Various rock bridges at varying stages of damage were also identified: intact (no
fracturing evidence observed along the rock bridge), damaged (single or cluster of cracks
observed within the intact rock of the rock bridge), and failed (presence of a fully persistent
fracture across the originally intact rock) (Figure 4d).
IRT dataset was collected with the FLIR SC7000 coupled with the f= 50 mm focal lens.
Thermal data showed limited or no seepage across the slope, likely due to the absence of
significant rainfall events in the days preceding the survey (28 August 2017). A significantly

Remote Sens. 2023,15, 3702 8 of 22
wetter area was noted, however, at an outcropping area in the lower part of the slope (inset
in Figure 5a). At least part of the seepage appeared to come from an exfoliation joint, which
may be fed by the fracture network located below the water table.
Remote Sens. 2023, 15, x FOR PEER REVIEW 8 of 22
the slope (inset in Figure 5a). At least part of the seepage appeared to come from an
exfoliation joint, which may be fed by the fracture network located below the water table.
(a)
(b)
(c)
Figure 5. Detail of the infrared datasets collected at the Yak Peak: (a) IRT dataset, displayed in
greyscale. Darker tones indicate colder temperatures. The inset magnifies the area in the lower part
of the slope where seepage is observed. Yellow box outlines the area depicted in (b,c). (b) Detail of
the HSI dataset, showing variability in electromagnetic irradiance along the slope surface (unclear
origin, e.g., seepage and runoff water during and after rainfall events, lichen growth, and newly
exposed surface), and the seepage in the lower part of the slope. (c) Sketch map highlighting the
various materials and features that can be distinguished in the high-resolution photograph. The
map highlights the role of partial arches and other structural features (marked by the black curves)
in controlling the spatial distribution of weathered or altered surfaces (orange areas).
The HSI dataset allowed different degrees of weathering across the slope to be
identified. Changes in the degree of weathering were observed throughout the slope,
which resemble areas of seepage and surficial water flow during prolonged rainfall
(Figure 5b,c). Specifically, the radiance of the slope appears to increase where surficial
flow typically occurs compared to areas that remain predominantly dry. The absence of
spectral targets at the slope surface prevented the calibration of the HSI image, and a
mineralogical classification of the surface was thus not performed. As a result, it is
Figure 5.
Detail of the infrared datasets collected at the Yak Peak: (
a
) IRT dataset, displayed in
greyscale. Darker tones indicate colder temperatures. The inset magnifies the area in the lower part
of the slope where seepage is observed. Yellow box outlines the area depicted in (
b
,
c
). (
b
) Detail of
the HSI dataset, showing variability in electromagnetic irradiance along the slope surface (unclear
origin, e.g., seepage and runoff water during and after rainfall events, lichen growth, and newly
exposed surface), and the seepage in the lower part of the slope. (
c
) Sketch map highlighting the
various materials and features that can be distinguished in the high-resolution photograph. The map
highlights the role of partial arches and other structural features (marked by the black curves) in
controlling the spatial distribution of weathered or altered surfaces (orange areas).
The HSI dataset allowed different degrees of weathering across the slope to be iden-
tified. Changes in the degree of weathering were observed throughout the slope, which
resemble areas of seepage and surficial water flow during prolonged rainfall (Figure 5b,c).
Specifically, the radiance of the slope appears to increase where surficial flow typically
occurs compared to areas that remain predominantly dry. The absence of spectral targets at

Remote Sens. 2023,15, 3702 9 of 22
the slope surface prevented the calibration of the HSI image, and a mineralogical classifica-
tion of the surface was thus not performed. As a result, it is currently unclear whether this
is due to variable mineral alteration or lichen growth, and further field and/or laboratory
analyses should be undertaken to verify and validate the long-range hyperspectral survey
results. Spatial correlations with newly exposed areas where recent rock block detach-
ment occurred, potentially associated with fresh, unaltered surfaces [
58
,
59
], may also be
investigated using multi-temporal datasets.
3.2. Mt. Kidd (Alberta, Canada)
Mt. Kidd is an iconic mountain peak located in south-eastern Alberta (western
Canada), within the Kananaskis National Park, 70 km west of the city of Calgary (
Figure 6a
).
The most prominent feature of Mt. Kidd is the namesake syncline fold that forms its south-
ernmost peak, which can be observed from the flood plain of the Marmot Creek along
Highway 40 (Figure 6b,c).
Remote Sens. 2023, 15, x FOR PEER REVIEW 9 of 22
currently unclear whether this is due to variable mineral alteration or lichen growth, and
further field and/or laboratory analyses should be undertaken to verify and validate the
long-range hyperspectral survey results. Spatial correlations with newly exposed areas
where recent rock block detachment occurred, potentially associated with fresh, unaltered
surfaces [58,59], may also be investigated using multi-temporal datasets.
3.2. Mt. Kidd (Alberta, Canada)
Mt. Kidd is an iconic mountain peak located in south-eastern Alberta (western
Canada), within the Kananaskis National Park, 70 km west of the city of Calgary (Figure
6a). The most prominent feature of Mt. Kidd is the namesake syncline fold that forms its
southernmost peak, which can be observed from the flood plain of the Marmot Creek
along Highway 40 (Figure 6b,c).
(a)
(b)
(c)
Figure 6. Overview of Mt. Kidd area: (a,b) location of Mt. Kidd in western Alberta (Canada) near
the border with British Columbia (red star in the inset), with the red square outlines the area
depicted in (b); (b) location of the survey location (yellow dot) with respect to the investigated slope
(outlined in yellow); and (c) view of the Mt. Kidd syncline from South. The inset shows an example
of incipient instability promoted by the structural seing.
The syncline involves the limestone, shales, siltstones, and dolostone of the
Etherington, Mount Head, Livingstone, and Banff formations, which are Mississippian in
age (359–323 million years) [60]. The syncline occurs at the termination zone of the Lewis
Thrust, which formed in relation to the east-west tectonic compression, caused by terrane
accretion, that resulted in the formation of the Canadian Cordillera [61,62]. The rock slope
where the syncline is displayed extends between the elevations of 1500 and 2700 m a.s.l.,
with a slope angle varying between 30° and 35°.
At the Mt. Kidd site, we collected TLS, HRP, IRT, and HSI data (see Section 2.1 for
details on the equipment used). A three-dimensional mesh was created from the TLS point
Figure 6.
Overview of Mt. Kidd area: (
a
,
b
) location of Mt. Kidd in western Alberta (Canada) near the
border with British Columbia (red star in the inset), with the red square outlines the area depicted
in (b)
; (
b
) location of the survey location (yellow dot) with respect to the investigated slope (outlined
in yellow); and (
c
) view of the Mt. Kidd syncline from South. The inset shows an example of incipient
instability promoted by the structural setting.
The syncline involves the limestone, shales, siltstones, and dolostone of the Ether-
ington, Mount Head, Livingstone, and Banff formations, which are Mississippian in age
(359–323 million years) [
60
]. The syncline occurs at the termination zone of the Lewis
Thrust, which formed in relation to the east-west tectonic compression, caused by terrane
accretion, that resulted in the formation of the Canadian Cordillera [
61
,
62
]. The rock slope
where the syncline is displayed extends between the elevations of 1500 and 2700 m a.s.l.,
with a slope angle varying between 30◦and 35◦.

Remote Sens. 2023,15, 3702 10 of 22
At the Mt. Kidd site, we collected TLS, HRP, IRT, and HSI data (see Section 2.1 for
details on the equipment used). A three-dimensional mesh was created from the TLS point
cloud. The other two-dimensional datasets were used to create textures, which were then
parametrized (i.e., registered) onto the mesh using the software Meshlab 2022.02. The
multi-texture model was then exported in a wavefront “.obj” format and visualized in the
HL headset using a viewer application specifically developed in Unity. The data collection
at Mt. Kidd was performed in order to demonstrate the ability to visualize a multi-sensor
dataset using the HL dataset (Figure 7).
Remote Sens. 2023, 15, x FOR PEER REVIEW 10 of 22
cloud. The other two-dimensional datasets were used to create textures, which were then
parametrized (i.e., registered) onto the mesh using the software Meshlab 2022.02. The
multi-texture model was then exported in a wavefront “.obj” format and visualized in the
HL headset using a viewer application specifically developed in Unity. The data collection
at Mt. Kidd was performed in order to demonstrate the ability to visualize a multi-sensor
dataset using the HL dataset (Figure 7).
(a)
(b)
(c)
Figure 7. MR visualization of the RS datasets collected at Mt. Kidd: (a) three-dimensional mesh of
Mt. Kidd, derived from the TLS. The MR interface, which allows for texture toggling and model
zooming and rotation, is visible (photographs were obtained using the HL in-built digital camera);
(b) model with HSI texture; and (c) model with IRT texture. In (b,c), the holes within the texture are
due to the occlusions due to the RS survey location.
Potential applications of the three-dimensional model may include (a) a detailed,
multi-scale, from slope- to outcrop-scale, discontinuity mapping; (b) a characterization of
fracture intensity distribution, aimed at the investigation of its spatial variation as a
function of the distance from the fold axis; and (c) the analysis of changes in layer
thickness to demonstrate the application of RS methods in the classification of structural
geological folds. Additionally, the combination with high-resolution photography can
potentially allow for the investigation of incipient rockfall and other slope failures, based
on visual examination of the datasets (inset in Figure 6c).
Figure 7.
MR visualization of the RS datasets collected at Mt. Kidd: (
a
) three-dimensional mesh of
Mt. Kidd, derived from the TLS. The MR interface, which allows for texture toggling and model
zooming and rotation, is visible (photographs were obtained using the HL in-built digital camera);
(
b
) model with HSI texture; and (
c
) model with IRT texture. In (
b
,
c
), the holes within the texture are
due to the occlusions due to the RS survey location.
Potential applications of the three-dimensional model may include (a) a detailed,
multi-scale, from slope- to outcrop-scale, discontinuity mapping; (b) a characterization
of fracture intensity distribution, aimed at the investigation of its spatial variation as a
function of the distance from the fold axis; and (c) the analysis of changes in layer thickness
to demonstrate the application of RS methods in the classification of structural geological

Remote Sens. 2023,15, 3702 11 of 22
folds. Additionally, the combination with high-resolution photography can potentially
allow for the investigation of incipient rockfall and other slope failures, based on visual
examination of the datasets (inset in Figure 6c).
3.3. Jure landslide (Nepal)
The Jure landslide is a large 5.5 million m
3
destructive landslide which occurred on
2 August 2014, near Jure village, on the right slope of the Sunkoshi River valley in central
Nepal, 70 km northeast of Kathmandu (Figure 8a). The landslide involved low-grade meta-
morphic rocks (phyllite, phyllitic quartzite, and metasandstones) of the Kuncha formation,
which is part of the Lesser Himalayan tectonic zone [
63
–
65
]. Several houses were destroyed,
and the slope failure resulted in about 150 fatalities. The landslide deposit dammed the
Sunkoshi River, and a 3 km long landslide lake formed, which damaged hydropower plants
both upstream and downstream before the dam was artificially breached after 37 days [
66
].
An historical analysis conducted using satellite imagery highlighted that an active slope
deformation, which resulted in multiple, smaller mass wasting events, was ongoing prior
to the detachment of the 2014 landslide [67].
Remote Sens. 2023, 15, x FOR PEER REVIEW 11 of 22
3.3. Jure landslide (Nepal)
The Jure landslide is a large 5.5 million m3 destructive landslide which occurred on 2
August 2014, near Jure village, on the right slope of the Sunkoshi River valley in central
Nepal, 70 km northeast of Kathmandu (Figure 8a). The landslide involved low-grade
metamorphic rocks (phyllite, phyllitic quarite, and metasandstones) of the Kuncha
formation, which is part of the Lesser Himalayan tectonic zone [63–65]. Several houses
were destroyed, and the slope failure resulted in about 150 fatalities. The landslide deposit
dammed the Sunkoshi River, and a 3 km long landslide lake formed, which damaged
hydropower plants both upstream and downstream before the dam was artificially
breached after 37 days [66]. An historical analysis conducted using satellite imagery
highlighted that an active slope deformation, which resulted in multiple, smaller mass
wasting events, was ongoing prior to the detachment of the 2014 landslide [67].
(a)
(c)
(b)
Figure 8. Overview of Jure landslide: (a) location of the study area within Nepal’s province of
Bagmati in the Indian subcontinent (inset); (b) 2020 satellite image of the landslide area
(CNES/Airbus image); and (c) 2018 UAV-SfM three-dimensional of the landslide. Note the growth
of scree and vegetation in the lower slope in (b) compared to the earlier three-dimensional model.
The landslide has a surface area that extends 1500 m downslope and 500 m across,
between an elevation of 1575 m a.s.l. and the valley floor at 800 m a.s.l. The average slope
angle prior to the failure was 35° (Figure 8b).
The structural seing of the area is characterized by pervasive faulting in an E-W
orientation. The most important geological structures are represented by three regional
faults: the Main Frontal Thrust (MFT), the Main Boundary Thrust (MBT), and the Main
Central Thrust (MCT). The activity of these faults causes the rock mass to appear intensely
foliated, folded, and fractured [64].
Figure 8.
Overview of Jure landslide: (
a
) location of the study area within Nepal’s province of Bagmati
in the Indian subcontinent (inset); (
b
) 2020 satellite image of the landslide area (CNES/Airbus image);
and (
c
) 2018 UAV-SfM three-dimensional of the landslide. Note the growth of scree and vegetation in
the lower slope in (b) compared to the earlier three-dimensional model.

Remote Sens. 2023,15, 3702 12 of 22
The landslide has a surface area that extends 1500 m downslope and 500 m across,
between an elevation of 1575 m a.s.l. and the valley floor at 800 m a.s.l. The average slope
angle prior to the failure was 35◦(Figure 8b).
The structural setting of the area is characterized by pervasive faulting in an E-W
orientation. The most important geological structures are represented by three regional
faults: the Main Frontal Thrust (MFT), the Main Boundary Thrust (MBT), and the Main
Central Thrust (MCT). The activity of these faults causes the rock mass to appear intensely
foliated, folded, and fractured [64].
3.3.1. Remote Sensing Analysis
At the Jure landslide site, various RS datasets were collected at different times. Yearly
long-range, ground-based photogrammetric and TLS surveys were conducted between
2016 and 2019. Both TLS and ground-based HRP were collected from multiple stations
along the valley floor and from the opposite slope. Airborne HRP was also collected in
2016 from a helicopter, and in 2018 and 2019 using the DJI Mavik Pro UAV (Figure 8c).
HRP datasets were used to create panoramic images and three-dimensional SfM models
in Metashape.
Long-range terrestrial and airborne SfM and TLS datasets were used to perform slope
scale discontinuity mapping in CloudCompare 2.12. TLS datasets provided, in general,
a higher point density, although distinguishing smaller features remained a significant
challenge, which could be ameliorated by overlaying the SfM-derived textured mesh.
Slope-scale discontinuity mapping allowed major structural features to be mapped. It
was noted that the landslide scar is crossed by various structural features that potentially
controlled the extent and failure mechanism of the landslide. The southern and northern
release surfaces appear to be controlled by two sets of structural features, referred to as
SL1 and NL1, with orientation of 43
◦
/052
◦
and 65
◦
/240
◦
, respectively. A major structural
feature, X1, also crosses the landslide scar with an orientation of 46◦/089◦.
Rock mass jointing and foliation were also observed in the long range HRP datasets.
These were investigated using the SfM-derived three-dimensional model of an outcrop in
proximity to the landslide headscarp, built through a short range HRP survey. An outcrop
scale discontinuity mapping, performed in CloudCompare 2.12 using the “Compass 2.0”
plugin [
68
], allowed four discontinuity sets (DS-I to DS-IV) to be identified. DS-I, DS-II, and
DS-IV are high angle discontinuity sets with orientation 80
◦
/180
◦
, 80
◦
/110
◦
, and 60
◦
/250
◦
,
while DS-III represents the sub-horizontal foliation.
The multi-temporal RS surveys allowed for the post-landslide evolution of the slope
to be investigated. It was noted that significant instability characterized the headscarp in
the years after the main landslide. A change detection analysis conducted using the 2017,
2018, and 2019 datasets, considering the 2016 dataset as reference, evidenced a progressive
retrogression of the headscarp. In total, about 440,000 m
3
of material detached from the
upper slope between 2016 and 2019, with a peak between 2016 and 2017, when mass
wasting events involved a total volume of 230,000 m
3
. Figure 9a shows the changes that
globally affected the slope from 2016 to 2019.
3.3.2. 2D Rockfall Simulations
The code Rocfall2 [
69
] was used to perform rockfall simulation in two dimensions. The
simulations were conducted using a representative section, extracted from the RS datasets
using the profile tool in ArcMap [
70
] along the average maximum steepness of the slope.
The considered section was traced starting from the primary rockfall source area identified
through the RS change detection analysis. Coefficients of restitution were assigned to the
various parts of the section, depending on the type of hillslope material (bedrock, talus,
and talus with vegetation) identified based on field observation and a visual analysis of the
RS datasets [
67
]. The size and shape of the blocks considered for the simulations derived
from a point cloud analysis. Block angular and translational velocities as well as bounce
height can be tracked along the rockfall trajectory from detachment to full stop [67].

Remote Sens. 2023,15, 3702 13 of 22
Rockfall simulation results, for the considered section, showed a maximum bounce
height (i.e., distance between block and ground surface, measured along the vertical
direction) of 65 m at the source area, and another peak (45 m) immediately below a
morphological step in the lower slope. Translational velocity profile displayed significant
undulations in value and peaks at 75 m/s, while angular velocity increased constantly
after block detachment and peaked at 140 rad/s. Peaks for both translational and angular
velocities occurred at the impact of the block following the 45 m peak in bounce height.
Remote Sens. 2023, 15, x FOR PEER REVIEW 12 of 22
3.3.1. Remote Sensing Analysis
At the Jure landslide site, various RS datasets were collected at different times. Yearly
long-range, ground-based photogrammetric and TLS surveys were conducted between
2016 and 2019. Both TLS and ground-based HRP were collected from multiple stations
along the valley floor and from the opposite slope. Airborne HRP was also collected in
2016 from a helicopter, and in 2018 and 2019 using the DJI Mavik Pro UAV (Figure 8c).
HRP datasets were used to create panoramic images and three-dimensional SfM models
in Metashape.
Long-range terrestrial and airborne SfM and TLS datasets were used to perform slope
scale discontinuity mapping in CloudCompare 2.12. TLS datasets provided, in general, a
higher point density, although distinguishing smaller features remained a significant
challenge, which could be ameliorated by overlaying the SfM-derived textured mesh.
Slope-scale discontinuity mapping allowed major structural features to be mapped.
It was noted that the landslide scar is crossed by various structural features that
potentially controlled the extent and failure mechanism of the landslide. The southern and
northern release surfaces appear to be controlled by two sets of structural features,
referred to as SL1 and NL1, with orientation of 43°/052° and 65°/240°, respectively. A
major structural feature, X1, also crosses the landslide scar with an orientation of 46°/089°.
Rock mass jointing and foliation were also observed in the long range HRP datasets.
These were investigated using the SfM-derived three-dimensional model of an outcrop in
proximity to the landslide headscarp, built through a short range HRP survey. An outcrop
scale discontinuity mapping, performed in CloudCompare 2.12 using the “Compass 2.0”
plugin [68], allowed four discontinuity sets (DS-I to DS-IV) to be identified. DS-I, DS-II,
and DS-IV are high angle discontinuity sets with orientation 80°/180°, 80°/110°, and
60°/250°, while DS-III represents the sub-horizontal foliation.
The multi-temporal RS surveys allowed for the post-landslide evolution of the slope
to be investigated. It was noted that significant instability characterized the headscarp in
the years after the main landslide. A change detection analysis conducted using the 2017,
2018, and 2019 datasets, considering the 2016 dataset as reference, evidenced a progressive
retrogression of the headscarp. In total, about 440,000 m3 of material detached from the
upper slope between 2016 and 2019, with a peak between 2016 and 2017, when mass
wasting events involved a total volume of 230,000 m3. Figure 9a shows the changes that
globally affected the slope from 2016 to 2019.
(a)
(b)
Figure 9.
Application of the three-dimensional datasets for change detection and rockfall modelling.
(
a
) Results of the slope scale change detection analysis, conducted using the M3C2 approach. The
red squares (1–3) show the areas of the landslide crown where most of the geomorphic evolution
occurred. The enclosed values indicate the 2016–2019 computed surface change. The stars (A,B)
indicate areas within the landslide area where generalized erosion or deposition occurred within
the same time window. (
b
) Overview of the rockfall numerical simulation developed in Unity game
engine. Rockfall sources are selected based on the results from the change detection analysis. The
holographic interface also includes a menu to control visualization parameters and object visibility
(to the left) as well as the results from two-dimensional rockfall simulations conducted in RocFall2
(to the right).
3.3.3. MR Rockfall Simulations
The models collected using RS methods were used as input geometry for three-
dimensional rock fall simulations conducted using the Unity game engine, exploiting
the state-of-the-art, built-in physics engine that is capable of realistically simulating the
collision between solid bodies, which, in this case, are the falling block and the ground
surface. In the models, terrain colliders and material physics were used to indirectly set
varying coefficients of restitution across the slope surface as a function of surface materials.
Rockfall source areas were assigned in the model based on the results of the change
detection analysis, which identified three areas where material detached over the 2016–2019
time window. Rockfall simulations were displayed in MR using the Microsoft HL headset
(Figure 9), allowing for up- and down-scaling of the model, the observation of the rockfalls
as the blocks move down the slope, overlay of multiple RS datasets and analyses (e.g.,
change detection results), and additional numerical models [51].

Remote Sens. 2023,15, 3702 14 of 22
Results of two-dimensional rockfall simulations were also imported in raster for-
mat into Unity in order to perform a direct comparison between the two modelling ap-
proaches (Figure 9). A good agreement was noted between the two- and three-dimensional
simulation results. Only marginal differences were noted in translational velocity and
bounce height, which were slightly higher in the three-dimensional models compared with
two-dimensional
simulations in Rocfall, and in rotational velocities, which were slightly
higher in the two-dimensional analysis.
4. Potential Future Applications of MR and VR for Rock Mass Characterization and
Real-Time Data Collection and Processing
The capability of support and assistance (even in real time) from experienced pro-
fessionals, and the capability of the chosen technique to allow datasets to be reviewed
and the site to be digitally (or virtually) revisited, can have significant, positive impacts
on the reliability of geological data and interpretations. In this section, two innovative
software applications for data collection exploiting MR and VR are presented, which can
potentially assist in data collection, processing, and interpretation: the mapping suites
“CoreLogger MR and XRCoreShack” and EasyMineXR. The presented software, particu-
larly EasyMineXR, is currently undergoing field testing at various mine sites. The results of
such testing and the description of cases studies involving real time MR mapping will be
presented at a later stage.
4.1. CoreLogger MR and XRCoreShack
CoreLogger MR is a novel software application that was developed for use with
Microsoft HL and core logging. The app allows the user to log physical cores, through
an MR interface that includes a holographic logging sheet in which values and param-
eters are inputted through a virtual or a Bluetooth keyboard (Figure 10a). Graphical
numerical data sheets, such as RMR and GSI, can also be clicked holographically
to store representative values for specific intervals. The HoloLens 2 finger tracking
capabilities are also used to acquire RQD values holographically by using them as
pivot points (Figure 10b). The user can also take advantage of the MR mesh scanning
proficiency to acquire RQD values directly along the core by placing the anchor point
directly on the core samples holographically.
Remote Sens. 2023, 15, x FOR PEER REVIEW 14 of 22
4. Potential Future Applications of MR and VR for Rock Mass Characterization and
Real-Time Data Collection and Processing
The capability of support and assistance (even in real time) from experienced
professionals, and the capability of the chosen technique to allow datasets to be reviewed
and the site to be digitally (or virtually) revisited, can have significant, positive impacts
on the reliability of geological data and interpretations. In this section, two innovative
software applications for data collection exploiting MR and VR are presented, which can
potentially assist in data collection, processing, and interpretation: the mapping suites
“CoreLogger MR and XRCoreShack” and EasyMineXR. The presented software,
particularly EasyMineXR, is currently undergoing field testing at various mine sites. The
results of such testing and the description of cases studies involving real time MR
mapping will be presented at a later stage.
4.1. CoreLogger MR and XRCoreShack
CoreLogger MR is a novel software application that was developed for use with
Microsoft HL and core logging. The app allows the user to log physical cores, through an
MR interface that includes a holographic logging sheet in which values and parameters
are inpued through a virtual or a Bluetooth keyboard (Figure 10a). Graphical numerical
data sheets, such as RMR and GSI, can also be clicked holographically to store
representative values for specific intervals. The HoloLens 2 finger tracking capabilities are
also used to acquire RQD values holographically by using them as pivot points (Figure
10b). The user can also take advantage of the MR mesh scanning proficiency to acquire
RQD values directly along the core by placing the anchor point directly on the core
samples holographically.
Individual cores and the whole core box can be digitized in 3D, using novel software
known as XRCoreShack. Within this software, multi-sensor core datasets can be
superimposed along and on the virtual core (Figure 10c). Datasets tested to date include
thermal, hyperspectral, FMI (full-bore formation microimager), assays, and previously
constructed core logs. The mesh can also be manipulated to observe structures and
mineralization that are not easily visible with the naked eye. The virtual thin section,
previously scanned and imaged, can also be implemented within the core database for
improved visualization (Figure 10d).
(a)
(b)
Figure 10. Cont.

Remote Sens. 2023,15, 3702 15 of 22
Remote Sens. 2023, 15, x FOR PEER REVIEW 15 of 22
(c)
(d)
Figure 10. Overview of geotechnical holographic core logging: (a) geotechnical core logging
performed with the CoreLoggerXR app, showing the interactive menus for data entry; (b)
measurement of the length of a rock core piece for RQD estimation; (c) section of a holographic core
piece ima ge d using opt ical (above) and hyperspectral sensors (below), with the hyperspectral model
shown in false colours; and (d) example of virtual thin section visualized within the MR app.
CoreLogger MR combined with XRCoreShack enables holographic core logging.
XRCoreShack has been implemented within VR platforms, allowing multiple core
samples to be assessed simultaneously, and enabling the comparison of different core
boxes side-by-side.
With recent advances in MR/VR and the development of XRCoreShack, re-
examination of core runs can be performed remotely, holographically, or virtually.
Reliance on digitizing core data has proven to be useful for auditable supervision, depth
adjustment via downhole geophysics, and rock parameter segregation via multi-
disciplinary assessment [71]. Furthermore, allowing core loggers to limit multi-step
logging procedures can reduce the uncertainty and errors associated with digitizing
information from paper records.
4.2. EasyMineXR
EasyMineXR (EMXR) is a Microsoft HL application developed under a joint Mitacs
Accelerate project between SRK Consulting Vancouver and Simon Fraser University, and
later commercialized [72,73].
EMXR uses the built-in 3D scanner of the Microsoft HL to capture data on site. The
Microsoft HL scanner creates a mesh of the object with a resolution of about 5 cm when
observed by the user from a distance of 0.8 m to 3 m. The engineer or geoscientist can
create a mesh in near-real time by walking along and looking at the outcrops of interest.
The user can also view, hide, and colorize the mesh. After completing the scan, the user
can see a small-scale preview of the mesh and save the 3D dataset. Georeferencing can be
conducted using a compass and a survey point or two survey points. Once the registration
process is completed, the local HL coordinate system will be converted to a real-world
coordinate system. The georeferenced data, such as meshes and drillhole data from
Leapfrog Geo [74] after importing into EMXR, will appear in their real-world coordinates,
allowing the user to visualize previously collected data and make more advanced
interpretations on site.
The software allows users to map geological, structural, and geomechanical features
by drawing polylines along them (Figure 11a). The user can specify the type of feature
Figure 10.
Overview of geotechnical holographic core logging: (
a
) geotechnical core logging per-
formed with the CoreLoggerXR app, showing the interactive menus for data entry; (
b
) measurement
of the length of a rock core piece for RQD estimation; (c) section of a holographic core piece imaged
using optical (above) and hyperspectral sensors (below), with the hyperspectral model shown in false
colours; and (d) example of virtual thin section visualized within the MR app.
Individual cores and the whole core box can be digitized in 3D, using novel soft-
ware known as XRCoreShack. Within this software, multi-sensor core datasets can be
superimposed along and on the virtual core (Figure 10c). Datasets tested to date include
thermal, hyperspectral, FMI (full-bore formation microimager), assays, and previously
constructed core logs. The mesh can also be manipulated to observe structures and
mineralization that are not easily visible with the naked eye. The virtual thin section,
previously scanned and imaged, can also be implemented within the core database for
improved visualization (Figure 10d).
CoreLogger MR combined with XRCoreShack enables holographic core logging. XR-
CoreShack has been implemented within VR platforms, allowing multiple core samples to be
assessed simultaneously, and enabling the comparison of different core boxes side-by-side.
With recent advances in MR/VR and the development of XRCoreShack, re-examination
of core runs can be performed remotely, holographically, or virtually. Reliance on digitizing
core data has proven to be useful for auditable supervision, depth adjustment via down-
hole geophysics, and rock parameter segregation via multi-disciplinary assessment [
71
].
Furthermore, allowing core loggers to limit multi-step logging procedures can reduce the
uncertainty and errors associated with digitizing information from paper records.
4.2. EasyMineXR
EasyMineXR (EMXR) is a Microsoft HL application developed under a joint Mitacs
Accelerate project between SRK Consulting Vancouver and Simon Fraser University, and
later commercialized [72,73].
EMXR uses the built-in 3D scanner of the Microsoft HL to capture data on site. The
Microsoft HL scanner creates a mesh of the object with a resolution of about 5 cm when
observed by the user from a distance of 0.8 m to 3 m. The engineer or geoscientist can create
a mesh in near-real time by walking along and looking at the outcrops of interest. The user
can also view, hide, and colorize the mesh. After completing the scan, the user can see a
small-scale preview of the mesh and save the 3D dataset. Georeferencing can be conducted
using a compass and a survey point or two survey points. Once the registration process is

Remote Sens. 2023,15, 3702 16 of 22
completed, the local HL coordinate system will be converted to a real-world coordinate
system. The georeferenced data, such as meshes and drillhole data from Leapfrog Geo [
74
]
after importing into EMXR, will appear in their real-world coordinates, allowing the user
to visualize previously collected data and make more advanced interpretations on site.
The software allows users to map geological, structural, and geomechanical features
by drawing polylines along them (Figure 11a). The user can specify the type of feature
before saving the trace and can also add annotations to the shape using a data-entry
form that appears when the 3D polyline is selected. The data-entry form template varies
depending on the structure type (e.g., lithological contact, fault, or joint). Traces are
exported directly as georeferenced .csv or .dxf files to geological and mining software
without any post-processing.
Remote Sens. 2023, 15, x FOR PEER REVIEW 16 of 22
before saving the trace and can also add annotations to the shape using a data-entry form
that appears when the 3D polyline is selected. The data-entry form template varies
depending on the structure type (e.g., lithological contact, fault, or joint). Traces are
exported directly as georeferenced .csv or .dxf files to geological and mining software
without any post-processing.
(a)
(b)
Figure 11. Application of EasyMineXR for rock mass characterization: (a) real-time geological
mapping conducted in an underground seing; (b) off-site office-based rock mass characterization
in EMXR, using a three-dimensional model built using RS data collected at the Jure landslide site.
In the inset, a view of the slope model that is being investigated.
For discontinuity mapping, an orientation tool has been developed that can measure
the dip and dip direction of the discontinuity facets. Orientation of the discontinuity
surfaces is displayed when the users look directly at them. This data can be saved and
displayed on a stereonet by an air tap hand gesture. A Fuzzy K-mean algorithm [75] can
also be used to determine the discontinuity sets. Collected orientation data can be
exported as .csv file for use in geotechnical and geological software. Current on-site
mapping methods require geologists and engineers to physically place their compasses or
cell phones on the rock face, which can be a slow process, and often comes with associated
safety issues. EMXR improves speed and safety by not requiring users to touch the
outcrop.
Finally, EMXR can take photographs and record videos. It can also show charts, such
as Barton and Choubey’s joint roughness profiles [76]. For measuring structure sizes, a
ruler tool is also available to the user.
Figure 11.
Application of EasyMineXR for rock mass characterization: (
a
) real-time geological
mapping conducted in an underground setting; (
b
) off-site office-based rock mass characterization in
EMXR, using a three-dimensional model built using RS data collected at the Jure landslide site. In the
inset, a view of the slope model that is being investigated.
For discontinuity mapping, an orientation tool has been developed that can measure
the dip and dip direction of the discontinuity facets. Orientation of the discontinuity
surfaces is displayed when the users look directly at them. This data can be saved and
displayed on a stereonet by an air tap hand gesture. A Fuzzy K-mean algorithm [
75
] can also
be used to determine the discontinuity sets. Collected orientation data can be exported as
.csv file for use in geotechnical and geological software. Current on-site mapping methods
require geologists and engineers to physically place their compasses or cell phones on

Remote Sens. 2023,15, 3702 17 of 22
the rock face, which can be a slow process, and often comes with associated safety issues.
EMXR improves speed and safety by not requiring users to touch the outcrop.
Finally, EMXR can take photographs and record videos. It can also show charts, such
as Barton and Choubey’s joint roughness profiles [
76
]. For measuring structure sizes, a
ruler tool is also available to the user.
EMXR can collect and process geomechanical and structural data in the field directly,
but it can also import three-dimensional models from other RS methods (e.g., TLS, SfM).
This enables users to remotely (re)visit sites and perform or review rock mass character-
ization and discontinuity mapping. In Figure 11b, the model of an outcrop behind the
headscarp of the Jure landslide is visualized with the EMXR app in a safe environment. A
comparison of field, RS computer-based software and holographic discontinuity mapping
along the same rock slope shows a mean pole orientation difference of about 5
◦
between
discontinuity sets [51].
5. Discussion and Conclusions
Over the past two decades, advances in technology have allowed the visualization,
processing, and interpretation of complex three-dimensional datasets. In particular, the
capability of extracting geological information from models created using RS methods,
such as TLS and digital photogrammetric techniques, represented a turning point both in
professional practice and in academic research. Similarly, the recent development of MR
and VR represents a potential breakthrough that allows datasets to be observed through
an unprecedented immersive experience. Presently, however, the applications of these
visualization techniques have been largely limited to communication and recreation (e.g.,
videogame industry), while applications of MR and VR for the purpose of data analysis
and processing is still in an early stage of development. In this paper, we demonstrated that
advanced, immersive visualization methods can be effectively used for data processing
and real time rock engineering/geology data collection and interpretation (Table 1).
Table 1. Summary of the applications of various geovisualization methods described in this paper.
Yak Peak Mt. Kidd Jure Landslide XRCoreShack EasyMineXR
Remote sensing
data collection
•TLS
•HRP
•HIS
•IRT
•TLS
•HRP
•IRT
•HSI
•TLS
•HRP
•UAV-SfM
•HRP
•SfM
•IRT
•HSI
•In-built HL
scanning
capability,
remote sensing
data import
2D/3D
visualization and
processing
(on screen)
•Discontinuity
mapping
•Brittle damage
•Seepage
mapping
•Structural char-
acterization *
•Discontinuity
mapping *
•Discontinuity
mapping
•Rockfall
simulations
•Lithological and
mineralogical
characterization *
•Discontinuity
analysis *
Possibility to
export HL scans
to traditional
visualization systems
MR/VR
visualization -
HSI, IRT datasets
visualized as texture
on 3D mesh from
TLS dataset
•3D
holographic rock-
fall analysis
•Landslide
MR/VR database
2D remote sensing
datasets visualized as
texture on 3D mesh
from SfM models
Visualization of
remote sensing
datasets from
various sources
(e.g., SfM, TLS)
MR/VR
data processing - - Outcrop scale MR/VR
discontinuity mapping
Datasets visualized
using MR/VR headsets,
where mapping
is undertaken
Immersive, MR rock
mass characterization
using remote sensing
datasets and models
MR/VR real time
data collection
and processing
- - -
Real time rock core
logging using virtual,
interactive sheets
and charts
Real time
discontinuity
mapping, annotation,
stereonet analysis
*: potential applications not demomnstrated in this paper.
The digital visualization and processing of geological data are today almost exclusively
conducted using traditional approaches (i.e., on screen). However, using MR and VR,
particularly to visualize three-dimensional models derived from RS datasets, allows the

Remote Sens. 2023,15, 3702 18 of 22
user to visit, walk around, and inspect outcrops that cannot be physically reached, and
to perform rock mass characterization and discontinuity mapping in a safe environment.
The ability to share the MR and VR experience in real time between people physically
located at different locations can enhance both real-time collaboration between engineers
and geoscientists as well as data visualization, providing significant improvements to the
quality of geological investigation and interpretation. Indeed, despite the importance of
rock mass characterization in the design of engineering structures, it is often conducted,
in the field and by junior staff with limited experience. Even standardized procedures for
rock mass and discontinuity classification and characterization, such as RQD (rock quality
designation, [
77
]), Q-System [
78
], and GSI [
2
], have potential for human error and bias that
can lead to crucial features being improperly logged (e.g., [
79
]). These errors may result in
increased project costs and improper design. We suggest that shared, in situ MR experience
can allow senior professionals to assist and train junior colleagues without being physically
present in the field, significantly optimizing mentoring using the collected experience of
consultants and reducing, at the same time, the potential for errors and bias.
Presently, the main challenge with the use of MR and VR methods lies in hardware
capability and portability. VR headsets are not stand-alone systems but require tethering
to a workstation: the specifications of the workstation control the quality and detail of
the models that can be visualized (Table 2). Additionally, VR headsets allow for a full
immersion in the virtual world, isolating the user from the real environment, potentially
inducing motion sickness and even posing safety issues due to a diminished awareness of
the user’s surroundings. Conversely, MR headsets, such as Microsoft HoloLens, allow full
portability and thus the use of the technology in the field. However, the miniaturization of
the components leads to a limited computational capability, thus limiting the quality and
dimension of the three-dimensional models that can be visualized at any given time.
Table 2.
Summary of the main advantages and limitations of current VR and MR visualization methods.
Advantages Challenges
Virtual
reality (VR)
•Fully immersive
•High computational capabilities
•Relatively low price range
•
High responsiveness controllers
•Possible motion sickness
•Eye straining
•Tethered to workstation
•Fish-eye effect
•
Limited awareness of surroundings
Mixed
reality (MR)
•Spatial awareness
•Untethered, stand-alone system
•Scanning capabilities
(Microsoft HoloLens)
•Computational limitations
•Battery life can be a concern
•Relatively high price range
Significant advancements in MR technology may be linked to the miniaturization of
an increasing number of powerful computer components, allowing for progressively more
detailed and complex three-dimensional models to be visualized.
A significant current limitation is related to the limited availability of MR/VR
commercial/open-source mapping software. In this paper, we presented EMXR, a Win-
dows software that allows geomechanical mapping of outcrops. However, the use of MR
technology for applications beyond rock mass characterization and discontinuity mapping
may require custom programs to be designed and coded in C# within Unity (or other
software or another game engine (e.g., Unreal Engine)) directly by the potential user, who
needs to be (or become) familiar with computer programming and coding. However, it
is expected that the increasing distribution of MR and VR headsets will accelerate the
development and expand the availability of both proprietary commercial and open-source
software for geotechnical applications.
The continuous development of RS techniques has greatly enhanced the ability of
geoscientists and engineers to perform geotechnical and geomechanical analyses (e.g., rock
mass characterization). Over the past two decades, however, it is apparent that these devel-
opments in data collection and processing procedures have dramatically outpaced that of

Remote Sens. 2023,15, 3702 19 of 22
innovative, inexpensive, and easy-to-use true 3D data visualization methods. As a result,
progressively more detailed and larger datasets” have been visualized using inherently
two-dimensional devices (e.g., computer screens and interactive displays). Despite the in-
troduction of “3D-ready” displays and the progressive increase in terms of pixel resolution,
these devices do not allow a true three-dimensional visualization of the collected datasets.
In this sense, the recent development and introduction of MR and VR systems represents
a significant upgrade in the field of geotechnical and geological data visualization. The
progressive miniaturization of electronic components and the dramatic increase in compu-
tational capabilities of computer systems will in the near future significantly enhance the
applicability of MR and VR technologies to geotechnical data analysis. In this paper, we
demonstrated the potential for advanced visualization techniques to enhance interpretation
of geological data. However, we emphasize that a reliable interpretation of field and RS
datasets through VR and MR methods cannot be possible without an adequate understand-
ing of the geological and geomorphological processes that can affect the investigated site as
well as their implications for its evolution.
Author Contributions:
Conceptualization, D.D. and D.S.; methodology, D.S., D.D., E.O., O.C. and
J.M.; software, O.C. and E.O.; formal analysis, D.D. and J.M.; investigation, D.D. and J.M.; resources,
D.S. and E.O.; writing—original draft preparation, D.D.; writing—review and editing, D.S., E.O., O.C.
and J.M.; visualization, E.O., O.C. and J.M.; supervision, D.S.; funding acquisition, D.S. All authors
have read and agreed to the published version of the manuscript.
Funding:
The authors would like to acknowledge the financial support provided through a NSERC
Discovery Grant (ID: RGPIN 05817) and FRBC Endowment funds provided to Doug Stead. EasyMineXR
software was developed under a Mitacs Accelerate Grant as a collaborative research project between
SRK Vancouver and Simon Fraser University.
Data Availability Statement:
No new data was created or analyzed in this study. Data sharing is not
applicable to this article.
Acknowledgments:
We acknowledge Ram Sherestha from the National Society for Earthquake
Technology and Nick Rosser from the Durham University (UK), for their assistance in the organization
of activities in Nepal, and Jack Williams for assistance at the Jure landslide site. We also acknowledge
Luca Zorzi for his assistance in the development and testing of EasyMineXR.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
Einstein, H.H.; Veneziano, D.; Baecher, G.B.; O’Reilly, K.J. The Effect of Discontinuity Persistence on Rock Slope Stability. Int. J.
Rock Mech. Min. Sci. Geomech. Abstr. 1983,20, 227–236. [CrossRef]
2.
Hoek, E.; Brown, E.T. The Hoek–Brown Failure Criterion and GSI—2018 Edition. J. Rock Mech. Geotech. Eng.
2019
,11, 445–463.
[CrossRef]
3.
ISRM Suggested Methods for the Quantitative Description of Discontinuities in Rock Masses. Int. J. Rock Mech. Min. Sci. Geomech.
Abstr. 1978,15, 319–368. [CrossRef]
4. Barton, N. The Shear Strength of Rock and Rock Joints. Int. J. Rock Mech. Min. Sci. Geomech. Abstr. 1976,13, 255–279. [CrossRef]
5.
Stead, D.; Donati, D.; Wolter, A.; Sturzenegger, M. Application of Remote Sensing to the Investigation of Rock Slopes: Experience
Gained and Lessons Learned. ISPRS Int. J. Geo-Inf. 2019,8, 296. [CrossRef]
6.
Sturzenegger, M.; Stead, D. Close-Range Terrestrial Digital Photogrammetry and Terrestrial Laser Scanning for Discontinuity
Characterization on Rock Cuts. Eng. Geol. 2009,106, 163–182. [CrossRef]
7.
Donati, D.; Stead, D.; Brideau, M.A.; Ghirotti, M. Using Pre-Failure and Post-Failure Remote Sensing Data to Constrain the
Three-Dimensional Numerical Model of a Large Rock Slope Failure. Landslides 2021,18, 827–847. [CrossRef]
8.
Jaboyedoff, M.; Oppikofer, T.; Abellán, A.; Derron, M.-H.; Loye, A.; Metzger, R.; Pedrazzini, A. Use of LIDAR in Landslide
Investigations: A Review. Nat. Hazards 2012,61, 5–28. [CrossRef]
9.
Guerin, A.; Jaboyedoff, M.; Collins, B.D.; Derron, M.H.; Stock, G.M.; Matasci, B.; Boesiger, M.; Lefeuvre, C.; Podladchikov, Y.Y.
Detection of Rock Bridges by Infrared Thermal Imaging and Modeling. Sci. Rep. 2019,9, 13138. [CrossRef]
10.
Abellán, A.; Jaboyedoff, M.; Oppikofer, T.; Vilaplana, J.M. Detection of Millimetric Deformation Using a Terrestrial Laser Scanner:
Experiment and Application to a Rockfall Event. Nat. Hazards Earth Syst. Sci. 2009,9, 365–372. [CrossRef]

Remote Sens. 2023,15, 3702 20 of 22
11.
Atzeni, C.; Barla, M.; Pieraccini, M.; Antolini, F. Early Warning Monitoring of Natural and Engineered Slopes with Ground-Based
Synthetic-Aperture Radar. Rock Mech. Rock Eng. 2015,48, 235–246. [CrossRef]
12.
Corsini, A.; Castagnetti, C.; Bertacchini, E.; Rivola, R.; Ronchetti, F.; Capra, A. Integrating Airborne and Multi-Temporal Long-
Range Terrestrial Laser Scanning with Total Station Measurements for Mapping and Monitoring a Compound Slow Moving Rock
Slide. Earth Surf. Process. Landf. 2013,38, 1330–1338. [CrossRef]
13.
Stumpf, A.; Malet, J.P.; Allemand, P.; Pierrot-Deseilligny, M.; Skupinski, G. Ground-Based Multi-View Photogrammetry for the
Monitoring of Landslide Deformation and Erosion. Geomorphology 2015,231, 130–145. [CrossRef]
14.
Kenner, R.; Gischig, V.; Gojcic, Z.; Quéau, Y.; Kienholz, C.; Figi, D.; Thöny, R.; Bonanomi, Y. The Potential of Point Clouds for the
Analysis of Rock Kinematics in Large Slope Instabilities: Examples from the Swiss Alps: Brinzauls, Pizzo Cengalo and Spitze Stei.
Landslides 2022,19, 1357–1377. [CrossRef]
15.
Kromer, R.A.; Abellán, A.; Hutchinson, D.J.; Lato, M.; Chanut, M.-A.; Dubois, L.; Jaboyedoff, M. Automated Terrestrial Laser
Scanning with Near-Real-Time Change Detection—Monitoring of the Séchilienne Landslide. Earth Surf. Dyn.
2017
,5, 293–310.
[CrossRef]
16.
Williams, J.G.; Rosser, N.J.; Hardy, R.J.; Brain, M.J.; Afana, A.A. Optimising 4-D Surface Change Detection: An Approach for
Capturing Rockfall Magnitude–Frequency. Earth Surf. Dyn. 2018,6, 101–119. [CrossRef]
17.
Shan, J.; Toth, C.K. Topographic Laser Ranging and Scanning. Principles and Processing; Taylor & Francis Group: Boca Raton, FL, USA,
2008; ISBN 978-1-4200-5142-1.
18.
Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. “Structure-from-Motion” Photogrammetry: A Low-Cost,
Effective Tool for Geoscience Applications. Geomorphology 2012,179, 300–314. [CrossRef]
19.
Giordan, D.; Godone, D.; Baldo, M.; Piras, M.; Grasso, N.; Zerbetto, R. Survey Solutions for 3D Acquisition and Representation of
Artificial and Natural Caves. Appl. Sci. 2021,11, 6482. [CrossRef]
20.
Menegoni, N.; Giordan, D.; Perotti, C.; Tannant, D.D. Detection and Geometric Characterization of Rock Mass Discontinuities
Using a 3D High-Resolution Digital Outcrop Model Generated from RPAS Imagery—Ormea Rock Slope, Italy. Eng. Geol.
2019
,
252, 145–163. [CrossRef]
21.
Francioni, M.; Stead, D.; Clague, J.J.; Westin, A. Identification and Analysis of Large Paleo-Landslides at Mount Burnaby, British
Columbia. Environ. Eng. Geosci. 2018,24, 221–235. [CrossRef]
22.
Clark, M.R.; McCann, D.M.; Forde, M.C. Application of Infrared Thermography to the Non-Destructive Testing of Concrete and
Masonry Bridges. NDT E Int. 2003,36, 265–275. [CrossRef]
23.
Röper, T.; Greskowiak, J.; Massmann, G. Detecting Small Groundwater Discharge Springs Using Handheld Thermal Infrared
Imagery. Groundwater 2014,52, 936–942. [CrossRef]
24.
Baroˇn, I.; Beˇckovský, D.; Míˇca, L. Application of Infrared Thermography for Mapping Open Fractures in Deep-Seated Rockslides
and Unstable Cliffs. Landslides 2014,11, 15–27. [CrossRef]
25.
Teza, G.; Marcato, G.; Pasuto, A.; Galgaro, A. Integration of Laser Scanning and Thermal Imaging in Monitoring Optimization
and Assessment of Rockfall Hazard: A Case History in the Carnic Alps (Northeastern Italy). Nat. Hazards
2015
,76, 1535–1549.
[CrossRef]
26.
Pappalardo, G.; Mineo, S.; Zampelli, S.P.; Cubito, A.; Calcaterra, D. InfraRed Thermography Proposed for the Estimation of the
Cooling Rate Index in the Remote Survey of Rock Masses. Int. J. Rock Mech. Min. Sci. 2016,83, 182–196. [CrossRef]
27.
Mineo, S.; Pappalardo, G.; Rapisarda, F.; Cubito, A.; Maria, G.D. Integrated Geostructural, Seismic and Infrared Thermography
Surveys for the Study of an Unstable Rock Slope in the Peloritani Chain (NE Sicily). Eng. Geol. 2015,195, 225–235. [CrossRef]
28.
Frodella, W.; Morelli, S.; Gigli, G.; Casagli, N. Contribution of Infrared Thermography to the Slope Instability Characterization.
In Proceedings of the World Landslide Forum 3, Beijing, China, 2–6 June 2014; pp. 97–103.
29.
van der Meer, F.D.; van der Werff, H.M.A.; van Ruitenbeek, F.J.A.; Hecker, C.A.; Bakker, W.H.; Noomen, M.F.; van der Meijde, M.;
Carranza, E.J.M.; de Smeth, J.B.; Woldai, T. Multi- and Hyperspectral Geologic Remote Sensing: A Review. Int. J. Appl. Earth Obs.
Geoinf. 2012,14, 112–128. [CrossRef]
30.
Riaza, A.; Strobl, P.; Beisl, U.; Hausold, A.; Müller, A. Spectral Mapping of Rock Weathering Degrees on Granite Using
Hyperspectral DAIS 7915 Spectrometer Data. ITC J. 2001,3, 345–354. [CrossRef]
31.
Debba, P.; Carranza, E.J.M.; Stein, A.; van der Meer, F.D. Deriving Optimal Exploration Target Zones on Mineral Prospectivity
Maps. Math. Geosci. 2009,41, 421–446. [CrossRef]
32.
Kurz, T.H.; Buckley, S.J.; Howell, J. A Close-Range Hyperspectral Imaging for Geological Field Studies: Workflow and Methods.
Int. J. Remote Sens. 2013,34, 1798–1822. [CrossRef]
33.
Murphy, R.J.; Schneider, S.; Monteiro, S.T. Mapping Layers of Clay in a Vertical Geological Surface Using Hyperspectral Imagery:
Variability in Parameters of SWIR Absorption Features under Different Conditions of Illumination. Remote Sens.
2014
,6, 9104–9129.
[CrossRef]
34.
Calvin, W.M.; Pace, E.L. Mapping Alteration in Geothermal Drill Core Using a Field Portable Spectroradiometer. Geothermics
2016,61, 12–23. [CrossRef]
35. Milgram, P.; Kishino, F. A Taxonomy of Mixed Reality Visual Displays. IEICE Trans. Inf. Syst. 1994,E77, 1321–1329.
36. WayRay. Available online: https://wayray.com/ (accessed on 15 March 2023).
37.
Brepohl, P.C.A.; Leite, H. Virtual Reality Applied to Physiotherapy: A Review of Current Knowledge. Virtual Real.
2023
,27, 71–95.
[CrossRef]

Remote Sens. 2023,15, 3702 21 of 22
38.
Choi, S.; Jung, K.; Noh, S.D. Virtual Reality Applications in Manufacturing Industries: Past Research, Present Findings, and
Future Directions. Concurr. Eng. 2015,23, 40–63. [CrossRef]
39. Product—Clirio, Inc. Available online: https://clir.io/product/ (accessed on 15 March 2023).
40.
Onsel, E.; Chang, O.; Mysiorek, J.; Donati, D.; Stead, D.; Barnett, W.; Zorzi, L. Applications of Mixed and Virtual Reality
Techniques in Site Characterization Multi-Sensor Remote Sensing Techniques Site Characterization with MR/VR. In Proceedings
of the 26th Vancouver Geotechnical Society Symposium, Vancouver, BC, Canada, 31 May 2019.
41.
Chang, O. Application of Mixed and Virtual Reality in Geoscience and Engineering Geology. Master’s Thesis, Simon Fraser
University, Burnaby, BC, Canada, 2019. Available online: https://summit.sfu.ca/item/34761 (accessed on 15 March 2023).
42.
Riegl RiSCAN PRO 2.6. 2018. Available online: http://www.riegl.com/products/software-packages/riscan-pro/ (accessed on
15 March 2023).
43. Agisoft Agisoft Metashape 1.5. 2020. Available online: https://www.agisoft.com/ (accessed on 15 March 2023).
44.
FLIR ResearchIR 4.4. 2019. Available online: https://www.flir.com/support-center/Instruments/researchir/ (accessed on 15
March 2023).
45.
L3 Harris Geospatiale ENVI
®
Geospatial Image Analysis Software 5.5. 2019. Available online: https://www.l3harrisgeospatial.
com/Software-Technology/ENVI (accessed on 15 March 2023).
46.
Lichtenauer, J.F.; Sirmacek, B. A Semi-Automatic Procedure for Texturing of Laser Scanning Point Clouds with Google Streetview
Images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2015,40, 109–114. [CrossRef]
47.
Eaton, J.; Bateman, D.; Hauberg, S.; Webhring, R. GNU Octave Version 8.1.0 Manual: A High-Level Interactive Language for
Numerical Computations. 2023. Available online: https://octave.org/ (accessed on 20 May 2023).
48. Meshlab 2022.02. 2022. Available online: https://www.meshlab.net/ (accessed on 15 March 2023).
49.
CloudCompare. CloudCompare 2.12 [GPL Software]. 2022. Available online: https://www.danielgm.net/cc/ (accessed on 15
March 2023).
50.
Donati, D.; Stead, D.; Lato, M.; Gaib, S. Spatio-Temporal Characterization of Slope Damage: Insights from the Ten Mile Slide,
British Columbia, Canada. Landslides 2020,17, 1037–1049. [CrossRef]
51.
Mysiorek, J.; Onsel, E.; Stead, D.; Rosser, N.J. Engineering Geological Characterization of the 2014 Jure Landslide, Nepal:
An Interactive Mixed-Reality Approach to Slope Characterization. In Proceedings of the Under Land and Sea. GeoStJohn
2019—72nd Canadian Geotechnical Conference, St. John’s, NL, Canada, 29 September–2 October 2019.
52. Unity Unity—Game Engine 2020. Available online: https://www.unity.com/ (accessed on 15 March 2023).
53. Blender Development Team Blender 2.93 LTS. 2022. Available online: https://www.blender.org/ (accessed on 15 March 2023).
54. Cairnes, C.E. Coquihalla Area, British Columbia; FA Acland: Ottawa, ON, Canada, 1924; 193p.
55.
Berman, R.G.; Armstrong, R.L. Geology of the Coquihalla Volcanic Complex, Southwestern British Columbia. Can. J. Earth Sci.
1980,17, 985–995. [CrossRef]
56.
Tuckey, Z. An Integrated Field Mapping-Numerical Modelling Approach to Characterising Discontinuity Persistence and Intact
Rock Bridges in Large Open Pit Slopes. M.Sc. Thesis, Simon Fraser University, 2012. Available online: https://summit.sfu.ca/
item/12708 (accessed on 10 April 2023).
57.
Leith, K.; Moore, J.R.; Amann, F.; Loew, S. In Situ Stress Control on Microcrack Generation and Macroscopic Extensional Fracture
in Exhuming Bedrock. J. Geophys. Res. 2014,119, 594–615. [CrossRef]
58.
Paronuzzi, P.; Bolla, A. In-Depth Field Survey of a Rockslide Detachment Surface to Recognise the Occurrence of Gravity-Induced
Cracking. Eng. Geol. 2022,302, 106636. [CrossRef]
59. de Vilder, S.J.; Rosser, N.J.; Brain, M.J. Forensic Analysis of Rockfall Scars. Geomorphology 2017,295, 202–214. [CrossRef]
60.
McMechan, M.A. Rocky Mountain Foothills and Front Ranges in Kananaskis Country, West of Fifth Meridian, Alberta, Geological Survey
of Canada, “A” Series Map 1865A, 1995, 1 Sheet; Natural Resources Canada: Ottawa, ON, Canada, 1995. [CrossRef]
61.
Zechmeister, M.S.; Pannalal, S.; Elmore, R.D. A Multidisciplinary Investigation of Multiple Remagnetizations within the Southern
Canadian Cordillera, SW Alberta and SE British Columbia. Geol. Soc. Lond. Spec. Publ. 2012,371, 123–144. [CrossRef]
62.
Price, R.A. The Cordilleran Foreland Thrust and Fold Belt in the Southern Canadian Rocky Mountains. Geol. Soc. Lond. Spec. Publ.
1981,9, 427–448. [CrossRef]
63.
Upreti, B.N. An Overview of the Stratigraphy and Tectonics of the Nepal Himalaya. J. Asian Earth Sci.
1999
,17, 577–606.
[CrossRef]
64.
Dhital, M.R. Geology of the Nepal Himalaya; Regional Geology Reviews; Springer International Publishing: Cham, Switzerland,
2015; ISBN 978-3-319-02495-0.
65.
Champati ray, P.K.; Chattoraj, S.L. Sunkoshi Landslide in Nepal and Its Possible Impact in India: A Remote Sensing Based
Appraisal. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
Hyderabad, India, 9–12 December 2014; Copernicus GmbH: Göttingen, Germany, 2014; Volume XL–8, pp. 1345–1351.
66.
Shrestha, B.B.; Nakagawa, H. Hazard Assessment of the Formation and Failure of the Sunkoshi Landslide Dam in Nepal. Nat.
Hazards 2016,82, 2029–2049. [CrossRef]
67.
Mysiorek, J. Engineering Geological Characterization of the 2014 Jure Nepal Landslide: An Integrated Field, Remote Sensing-
Virtual/Mixed Reality Approach. Master’s Thesis, Simon Fraser University, Burnaby, BC, Canada, 2019. Available online:
https://summit.sfu.ca/item/19917 (accessed on 10 March 2023).

Remote Sens. 2023,15, 3702 22 of 22
68.
Thiele, S.T.; Grose, L.; Samsu, A.; Micklethwaite, S.; Vollgger, S.A.; Cruden, A.R. Rapid, Semi-Automatic Fracture and Contact
Mapping for Point Clouds, Images and Geophysical Data. Solid Earth 2017,8, 1241–1253. [CrossRef]
69. Rocscience RocFall2. 2022. Available online: https://www.rocscience.com/software/rocfall (accessed on 15 March 2023).
70. ESRI ArcMap 10.6. 2019. Available online: https://www.esri.com/ (accessed on 15 March 2023).
71.
Orpen, J.L. Best Practice and New Technology in Core Drilling, Logging and Fracture Analysis. In Proceedings of the 1st
International Conference on Discrete Fracture Network Engineering, Vancouver, BC, Canada, 19 October 2014.
72.
SRK Consulting EasyMineXR Collaborative Mapping Software. 2022. Available online: https://www.srk.com/en/products/
easyminexr-collaborative-mapping-software (accessed on 15 April 2023).
73.
Onsel, I.E.; Donati, D.; Stead, D.; Chang, O. Applications of Virtual and Mixed Reality in Rock Engineering. In Proceedings of the
52nd U.S. Rock Mechanics/Geomechanics Symposium, Washington, DC, USA, 17–20 June 2018.
74.
Seequent Leapfrog Geo. 2022. Available online: https://www.seequent.com/products-solutions/leapfrog-geo/ (accessed on 15
April 2023).
75.
Hammah, R.E.; Curran, J.H. On Distance Measures for the Fuzzy K-Means Algorithm for Joint Data. Rock Mech. Rock Eng.
1999
,
32, 1–27. [CrossRef]
76.
Barton, N.; Choubey, V. The Shear Strength of Rock Joints in Theory and Practice. Rock Mech. Felsmech. Mec. Roches
1977
,10, 1–54.
[CrossRef]
77.
Deere, D.U.; Deere, D.W. Rock Quality Designation (RQD) after Twenty Years; Defense Techniccal Information Centre: Fort Belvoir,
VA, USA, 1989; ADA207597. Available online: https://apps.dtic.mil/sti/citations/ADA207597 (accessed on 15 April 2023).
78. NGI. Using the Q-System. Rock Mass Classification and Support Design; NGI: Oslo, Norway, 2022.
79.
Pells, P.J.; Bieniawski, Z.T.; Hencher, S.R.; Pells, S.E. Rock Quality Designation (RQD): Time to Rest in Peace. Can. Geotech. J.
2017
,
54, 825–834. [CrossRef]
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