Virtual Outcrops in a Pocket: Smartphone as a Fully Equipped Photogrammetric Data Acquisition Tool - 10-13 Oct. GSA Connects 2021
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10–13 Oct. GSA Connects 2021
VOL. 31, NO. 9 | SEPTEMBER 2021
Virtual Outcrops in a Pocket:
Smartphone as a Fully Equipped
Photogrammetric Data Acquisition Tool
Virtual Outcrops in a Pocket:
The Smartphone as a Fully Equipped
Photogrammetric Data Acquisition Tool
Amerigo Corradetti*, Dept. of Mathematics and Geosciences, University of Trieste, Trieste, Italy; Thomas D. Seers, Dept. of Petroleum
Engineering, Texas A&M University at Qatar, Doha, Qatar; Andrea Billi, Consiglio Nazionale delle Ricerche, IGAG, Rome, Italy; and
Stefano Tavani, Consiglio Nazionale delle Ricerche, IGAG, Rome, Italy, and DiSTAR, Università di Napoli Federico II, Napoli, Italy
ABSTRACT seismically active fault. The scan is con- grammetry by outcrop geologists was ini-
Since the advent of affordable consumer- ducted with minimal effort over the course tially slow (e.g., Hodgetts et al., 2004; Pringle
grade cameras over a century ago, photo- of a few minutes with limited equipment, et al., 2004), with legacy photogrammetric
graphic images have been the standard thus being representative of a routine situa- reconstruction techniques requiring highly
medium for capturing and visualizing out- tion for a field geologist. specialized, expensive metric cameras or
crop-scale geological features. Despite the software (Chandler and Fryer, 2005), and
ubiquity of raster image data capture in rou- INTRODUCTION AND commonly carried the limitation of cumber-
tine fieldwork, the development of close- BACKGROUND some manual assignment of key points on
range 3D remote-sensing techniques has led Rapid improvements in the fidelity of con- the targeted rock surface (e.g., Simpson et al.,
to a paradigm shift in the representation and sumer-grade cameras, coupled with novel 2004). Many of these disadvantages were
analysis of rock exposures from two- to computer vision–based photogrammetric addressed with the advent of low-cost or
three-dimensional forms. The use of geolog- image processing pipelines (i.e., structure open-source Sf M-MVS photogrammetry
ical 3D surface reconstructions in routine from motion–multiview stereo photogram- image processing pipelines (e.g., Snavely et
fieldwork has, however, been limited by the metry: SfM-MVS), have revolutionized out- al., 2006; Furukawa and Ponce, 2009; Wu,
portability, associated learning curve, and/ crop studies over the past decade, bringing 2011), which facilitated the use of uncali-
or expense of tools required for data capture, traditional field geology into the digital age. brated consumer-grade cameras and enabled
visualization, and analysis. Smartphones These developments are also closely tied to automated image key-point detection and
are rapidly becoming a viable alternative to major methodological improvements for vir- matching (e.g., James and Robson, 2012).
conventional 3D close-range remote-sensing tual outcrop model (VOM) interpretation. The potential of producing 3D rock-surface
data capture and visualization platforms, All these advancements have accelerated the models using consumer-grade cameras
providing a catalyst for the general uptake of use of digital outcrop data capture and analy- attracted the interest of numerous workers.
3D outcrop technologies by the geological sis in field geology, transforming what was These developments coupled with the increas-
community, which were up until relatively principally a visualization medium into fully ing availability of lightweight and low-cost
recently the purview of a relatively small interrogatable quantitative geo-data objects drones able to carry cameras and other sen-
number of geospatial specialists. Indeed, the (Jones et al., 2004; Bemis et al., 2014; Howell sors, have finally boosted the use of SfM-
continuous improvement of smartphone et al., 2014; Hodgetts et al., 2015; Biber et al., MVS reconstruction in geosciences.
cameras, coupled with their integration with 2018; Bruna et al., 2019; Caravaca et al., For many geoscience applications, it is
global navigation satellite system (GNSS) 2019; Thiele et al., 2019; Triantafyllou et al., necessary to register 3D rock-surface recon-
and inertial sensors provides 3D reconstruc- 2019). Initially, close-range remote-sensing structions within a local or global coordinate
tions with comparable accuracy to survey- studies seeking to reconstruct and analyze frame. The use of survey-grade total stations
grade systems. These developments have rock outcrops were dominantly built around and/or real-time kinematic (RTK) differen-
already led many field geologists to replace terrestrial laser scanning systems (terrestrial tial global navigation satellite system (GNSS)
reflex cameras, as well as dedicated hand- lidar), which became commercially available antennas permit both terrestrial (Jaud et al.,
held GNSS receivers and compass clinome- around two decades ago (e.g., Bellian et al., 2020) and aerial (Rieke et al., 2012) image
ters, with smartphones, which offer the 2002). These initial works tended to be tech- data and/or ground control points (GCPs) to
equivalent functionality within a single nology demonstrations rather than routine be georeferenced within the mapped scene
compact platform. Here we demonstrate that field studies, with the expense, weight, and with centimeter to millimeter accuracy
through the use of a smartphone and a por- challenging operational learning curve lim- (Bemis et al., 2014). Those survey tools are,
table gimbal stabilizer, we can readily gen- iting replication to a few highly specialized however, bulky and expensive, and are not
erate and register high-quality 3D scans of geospatial specialists and groups. Receiving standard tools for geoscientists engaged in
outcropping geological structures, with the greater interest from the archaeological fieldwork. Improvements in consumer-grade
workflow exemplified using a mirror of a community, the adoption of digital photo- GNSS receivers, capable of harnessing
GSA Today, v. 31, https://doi.org/10.1130/GSATG506A.1. CC-BY-NC.
*amerigo.corradetti@units.it
4 GSA Today | September 2021
multiple constellations (i.e., GPS, Glonass,
Galileo, and BeiDou), now permit model
geo-registration with greater simplicity and
accuracies that are acceptable for many geo-
scientific applications. Most current smart-
phones are equipped with such GNSS chip-
sets, which enable the positioning of photos
and GCPs with meter-level accuracy, or even
spatial-decimeter accuracy for dual-fre-
quency chipsets, with >20 min acquisition
times for individual locations (Dabove et al.,
2020; Uradziński and Bakuła, 2020). Under
these conditions, the use of smartphones per-
mits georeferencing of >~100-m-wide pho-
togrammetric models generated via terres-
trial imagery (Fig. 1). The availability of
Figure 1. Scale-ranges of applicability of different methods for the registration of 3D models of out-
photo orientation information, provided by crops, and tools used in this work. GCPs—ground control points; GNSS—global navigation satellite
the smartphone’s inertial measurement unit system; RTK—real-time kinematic.
(especially the magnetometer and gyro-
scope/accelerometer sensors), in conjunction
with the GNSS position, can further improve
the quality of the model registration proce-
dure. Indeed, the photo orientation informa-
tion mitigates the positional error associated
with the Z component, and full georeferenc-
ing of >50–60-m-wide exposures can be
achieved with a consumer-grade dual-fre-
quency GNSS chipset–equipped smartphone
(Tavani et al., 2019, 2020).
Confident georeferencing of smaller-scale
outcrops with minimal equipment, however,
remains challenging, limiting the utility of
photogrammetric acquisition in routine geo-
logical fieldwork. In this article, we present a
workflow using a smartphone and minimal
accessories to address this challenge (Fig. 1)
and demonstrate the applicability of using
smartphone photo and video surveys of an
active fault in the Apennines (Italy). Those
3D models are georeferenced by integrating
the use of Agisoft Metashape and OpenPlot
software tools (Tavani et al., 2019).
METHODS AND DATA
The Acquisition Site
The survey method proposed herein was
performed on an outcrop of an active nor-
mal fault located within the Apennines,
central Italy. A high-resolution 3D surface
reconstruction of the outcrop is already
available (Corradetti et al., 2021), thus
allowing us to compare our results with a
ground-truth model. The area contains out- Figure 2. Photograph of the active normal fault modeled in this work (A). (B) Field set up and measure-
cropping Mesozoic rocks affected by active ments taken before image acquisition. A ruler is used to measure the length between two points, each
photographed for later recognition. A stand (compass holder, CH) is placed on the outcrop and its
normal faulting. For the aforementioned attitude measured defining the CH strike. The operator can then proceed with the photo/video acqui-
survey, we focused upon one segment strik- sition providing that the CH is left on the outcrop to be included in the model. (C) Dense point cloud of
the Photo Model. In the model, four markers are added, representing the two points whose distance
ing N135°–160° (Fig. 2A). A wide (~0.3–1 m) was measured with the tape, and two points along the CH strike. The θ, ξ, and ρ vectors of the images
portion of this fault was exposed after the are also indicated.
www.geosociety.org/gsatoday 5
dramatic MW 6.5 earthquake that struck the 2 represent the two points whose distance
area on 30 Oct. 2016 (e.g., Chiaraluce et al., was manually measured in the field. Point 3
2017), offering the opportunity to study this and Point 4 were instead picked along one
“fresh” portion of the fault surface (the edge of the digitized compass holder (CH;
white ribbon shown over the bottom of the Fig. 2C). These are used to retrieve the
fault surface in Fig. 2A). trend of the CH strike, here coinciding with
the strike of the fault plane. The rotational
Pre-Acquisition Setup transformation is the most critical aspect of
Image acquisition was carried out on 30 model registration for many geoscience
Oct. 2020, between 12:46 p.m. and 1:01 p.m., applications (e.g., discontinuity, bedding
using a dual-frequency GNSS-equipped plane, or geobody orientation analysis). Our
smartphone (Xiaomi 9T pro), hand-held survey carries different assumptions for the
gimbal, compass holder, compass-clinome- orientation of photographs: the short axis of
ter, and metric tape measure (see Fig. 1). In the photo (θ in Fig. 2C) is pointing upward;
the field (Fig. 2B), the compass holder was the view direction (ξ in Fig. 2C) is gently
placed within the scene using a detachable plunging and at a high angle to the fault
sticky pad with its edge approximately hori- plane; the long axis of the photo (ρ in Fig.
zontal in relation to the Earth frame, and its 2C) is lying horizontal, due to gimbal stabi-
trend (CH strike in Fig. 2C) measured using lization. The goal is to use the stabilized
a Brunton TruArc 20 compass. The metric direction of the long axis of photos to regis-
tape was used to measure the distance ter the vertical axis and the markers placed
between two arbitrary features that later on the CH (defining the CH strike) to reori-
must be identified in the 3D model to pro- ent the model around this vertical axis. This
vide its scaling factor. Both the compass is done after exporting from Metashape the
and the metric measuring tape were removed cameras’ extrinsic parameters using the
before scene acquisition. N-View Match (*.nvm) file format. The
exported data include θ, ξ, and ρ vectors
Image Acquisition expressed in the arbitrary reference frame.
We produced two digital models of the Then, we exported the markers in *.txt for-
fault using different approaches. The first mat, which saves the estimated position of
model (from here on referred to as the Photo markers in the arbitrary reference frame.
Model) was generated using 200 photos These files are imported in OpenPlot, where
(4000 × 2250 pixels and 4.77 mm focal the photos’ directions and the CH strike are
length). The second model (from here on computed and graphed in a stereoplot (Plot
referred to as the Video Model) was built 1 in Fig. 3). For both Photo and Video mod-
using 528 photos (3840 × 2160 pixels and els, the ρ direction is clustered along a great
4.77 mm focal length) extracted using VLC circle, which, thanks to the gimbal, repre-
software from a 257-second-long video file sents the horizontal plane in the real-world
(i.e., 2.6 frames per second). Both acquisi- frame. For each model, the entire data set
tions were carried out using the smartphone (i.e., the three directions of photos and the
mounted on a DJI OM4 gimbal, at a dis- four markers) are rotated to set the ρ great
tance of ~30 cm from the fault plane. To circle horizontal (Plot 2 in Fig. 3). Notice
include images oblique to the fault plane, that the rotation axis is univocally defined,
required to mitigate doming of the recon- being coincident to the strike of the best-fit
structed scene (James and Robson, 2014; plane. The amount of rotation instead can
Figure 3. Lower hemisphere stereographic pro-
Tavani et al., 2019), the view direction was be either the dip of the plane or 180° + dip. jection (stereonet) of the camera vectors for both
repeatedly changed within an ~60° wide The correct placement of the view direction the Photo and Video models, after model building
cone. Nevertheless, avoiding operator- (ξ) means that the selection between these (Plot 1), and after horizontalization of the ρ-vector
great-circle envelope (Plot 2). In essence, after
induced shadows into the scene meant that two options by the user is trivial. The result- this rotation, the vertical axis is paralleled to the
the main acquisition was sub-perpendicular ing trend of the CH strike is N211° and true vertical, but the azimuth is yet randomly ori-
ented. (Plot 3) Stereonet of the camera vectors
to the strike of the fault, being ~ENE. N105° for the Photo and Video models, after rotation around the vertical axis. (Plot 4)
respectively. A rotation about the vertical Rose diagram showing the distribution of the ρ
vectors in both models. CH—compass holder.
Image Processing and Model axis (57° counterclockwise for the Photo
Registration Model and 49° clockwise for the Video
Images were processed in Agisoft Meta- Model) was applied to the entire data set to Point 2 and were eventually fully georefer-
shape (version 1.6.2), resulting in two unreg- match the CH strike to its measured value, enced using the measured position of
istered dense point clouds (Fig. 2C). Four i.e., N154° (Plot 3 in Fig. 3). The twice- Point 1. These two steps are achieved during
specific markers were manually added in rotated markers were then scaled using the the export stage from OpenPlot, which com-
Metashape. In Figure 2C, Point 1 and Point measured distance between Point 1 and piles a *.txt file containing the correctly
6 GSA Today | September 2021
georeferenced coordinates of the four mark- removed after alignment, improving the qual- Model) built in 2016 using an image survey
ers. This file was imported into Metashape, ity of the 3D scene reconstruction. These captured from the same outcrop with a
which allows the direct georeferencing of the images were identified through manual selec- dSLR camera (Fig. 4C). In this regard, the
model. The whole procedure, from the export tion of points associated with unrealistic or same fault was mapped in 2016 (Corradetti
or unregistered data from Metashape, blurry geometries within the sparse cloud. et al., 2021), using 640 images (4272 × 2848
through the rotations, scaling, and referenc- Often those were frames characterized by pixels) taken with a Canon EOS 450D reflex
ing in OpenPlot and the final re-import in extreme overlap. mounted on a tripod to suppress motion
Metashape takes just a few minutes and can Both point clouds are characterized by blur. The reconstructed area for the Reflex
be followed step-by-step in the supplemen- zones on their boundaries, in which the 3D Model was ~2.67 m 2, and the point cloud
tary video provided (see Supplementary scene reconstruction relies on oblique images included ~2.7 × 108 points. These three point
Material1). A good practice consists of check- (Fig. 4B). These zones are asymmetrical, clouds were uploaded in CloudCompare
ing the results and re-exporting the cameras’ due to the aforementioned obliquity between (Girardeau-Montaut, 2015), where they
extrinsic data of the registered model to pos- the fault-perpendicular direction and the were first manually aligned using ~15 con-
sibly repeat the procedure if residual rota- average photo view direction. Accordingly, trol points for each matched point cloud,
tions occur (i.e., if ρ is not perfectly lying on we cropped the point clouds to exclude and then they were compared using the
a horizontal plane), which may relate to the these zones and areas where the 3D recon- cloud-to-cloud distance tool. The resulting
proximity of the markers used for the trans- struction relied upon less than nine images distance among the three clouds was gener-
form and on their positional accuracy. (Fig. 4B). ally below 4 mm (Fig. 4D), which decreases
The cropped point cloud for the Photo down to
result is a transformation matrix indicating Two data sets, (i.e., photos and images discrepancy in the estimated horizontal plane
that to align the two point clouds, a scaling extracted from a video sequence) have been between the two models, considering the
factor of 1.0012 is required. The rotations tested to produce and later register the Photo orientation of the fault, is 1.1° around the
around the X, Y, and Z axes are –0.38°, and Video models, respectively. These models strike direction and 0.29° around the horizon-
1.00°, and 0.34° (1.1° around the strike direc- have been compared together and with the tal direction perpendicular to the fault’s strike.
tion and 0.29° around the horizontal direc- Reflex Model, which represents a benchmark In other words, the registration of the horizon-
tion perpendicular to the strike). build with photos obtained in 2016, although tal plane is sensitive to the orientation of the
probably minor morphological changes due to photographs, so that the inclusion of oblique
DISCUSSION weathering can have occurred since then. to the scene photographs may improve the
We have described a workflow for gener- Manual alignment of the Photo and Video “horizontalization” of ρ.
ating georeferenced 3D models of geologi- models shows that discrepancies ranging
cal outcrops ranging in size from tens of from 0 to 5 mm occur between the surface CONCLUSION
meters down to a few centimeters. The reconstructions. There are notable discrepan- This paper faces the need encountered by
required tools are extremely portable. Their cies between the Video and Reflex models, many field geologists to efficiently capture
use in the field is straightforward, with sur- whereas the Photo and Reflex models are images of outcrops with ultra-portable tools
vey acquisition taking a few minutes for our much more comparable, with surface dis- to produce detailed, scaled, and properly ori-
case study. During the development and placements ranging between 0 and 2 mm. ented “pocket” 3D digital representations of
testing of the procedure, it was notable that Despite the lower number of input photos, the rock exposures. Submillimeter point-cloud
video sequence acquisition can provide a Photo Model outperforms the Video Model in resolution is achieved with the suggested
more coherent scene, assuming that the terms of accuracy. The major reason for this is procedure, equaling that of models obtained
mapped area is relatively continuous. On the problematic reconstruction of the scene by means of reflex cameras, and proving the
the other hand, video sequences may gener- from extremely narrow baseline images efficiency of the proposed registration proce-
ate excessive scene overlap, complicating extracted from the video sequence. Despite dure for several quantitative applications in
image matching. Also, the use of video the video capture having a more straightfor- geology (e.g., fracture and fault orientation
frames implies the lack of control on shutter ward acquisition procedure, it may require a and associated kinematic indicators, bedding
speed, aperture, ISO, etc., limiting the use more complex and time-consuming user- attitude and thickness, fault roughness, etc.).
of video frames mostly to small outcrops. assisted procedure of image selection and Furthermore, the proposed method is intui-
Thus, selectively captured still images gen- repeated runs of photo alignment. tive so that it can be applied by all geoscien-
erally ensure a better result and a shorter Apart from minor differences in recon- tists irrespective of background or experi-
processing time, as long as the acquisition is struction quality and errors that may arise ence. In this regard, we hope that this
correctly carried out. Video models instead from manual detection of the key points used workflow will favor the widespread use of
provide a simpler acquisition scheme, albeit in the similarity transform, the registration 3D models from smartphones.
with greater risk of reconstruction artifacts. procedure of the two smartphone-generated
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