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Citation: Jaud, M.; Le Dantec, N.;
Parker, K.; Lemon, K.; Lendre, S.;
Delacourt, C.; Gomes, R.C. How to
Include Crowd-Sourced
Photogrammetry in a Geohazard
Observatory—Case Study of the
Giant’s Causeway Coastal Cliffs.
Remote Sens. 2022,14, 3243. https://
doi.org/10.3390/rs14143243
Academic Editor: Joong-Sun Won
Received: 24 May 2022
Accepted: 4 July 2022
Published: 6 July 2022
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4.0/).
remote sensing
Article
How to Include Crowd-Sourced Photogrammetry in a
Geohazard Observatory—Case Study of the Giant’s Causeway
Coastal Cliffs
Marion Jaud 1, 2, * , Nicolas Le Dantec 1,2, Kieran Parker 3, Kirstin Lemon 3, Sylvain Lendre 4,
Christophe Delacourt 1and Rui C. Gomes 5
1Laboratoire Geo-Oceans—UMR 6538, CNRS, University Brest, Rue Dumont D’Urville,
29280 Plouzané, France; nicolas.ledantec@univ-brest.fr (N.L.D.); christophe.delacourt@univ-brest.fr (C.D.)
2IUEM-UMS 3113, CNRS, University Brest, IRD, Rue Dumont D’Urville, 29280 Plouzané, France
3British Geological Survey of Northern Ireland, Dundonald House, Upper Newtownards Road, Ballymiscaw,
Belfast BT4 3SB, UK; kiepar@bgs.ac.uk (K.P.); klem@bgs.ac.uk (K.L.)
4CEREMA, Direction Eau Mer et Fleuves, 134 Rue de Beauvais, 60280 Margny-lès-Compiègne, France;
sylvain.lendre@cerema.fr
5CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal;
rui.carrilho.gomes@tecnico.ulisboa.pt
*Correspondence: marion.jaud@univ-brest.fr
Abstract:
The Causeway Coast World Heritage Site (Northern Ireland) is subject to rockfalls occurring
on the coastal cliffs, thus raising major safety concerns given the number of tourists visiting the
site. However, such high tourist frequentation makes this site favorable to implement citizen science
monitoring programs. Besides allowing for the collection of a larger volume of data, better distributed
spatially and temporally, citizen science also increases citizens’ awareness—in this case, about risks.
Among citizen science approaches, Structure-from-Motion photogrammetry based on crowd-sourced
photographs has the advantage of not requiring any particular expertise on the part of the operator
who takes photos. Using a mock citizen survey for testing purposes, this study evaluated different
methods relying on crowd-sourced photogrammetry to integrate surveys performed by citizens into
a landslide monitoring program in Port Ganny (part of the touristic site of the Giant’s Causeway).
Among the processing scenarios that were tested, the Time-SIFT method allows the use of crowd-
sourced data in a very satisfactory way in terms of reconstruction quality, with a standard deviation
of 8.6 cm.
Keywords: citizen science; SfM photogrammetry; risk assessment; rockfalls; Time-SIFT method
1. Introduction
Geohazards may lead to damage or risks for human beings or infrastructure. To better
understand and anticipate these hazards, it is necessary to know the triggering forcings
and precursor signals. This implies regular and distributed monitoring points along the
hazard-prone area. In practice, such monitoring can be difficult to implement.
With smartphones, almost all citizens are now equipped with potential sensors and
data transmission platforms. Participatory science programs have therefore increased in
recent years. Several worldwide support platforms have also been created to support
and/or structure citizen science program initiatives. These include, for example, CitSci
(https://www.citsci.org/ (accessed on 6 December 2021)), Citizen Science Association (https:
//citizenscience.org/ (accessed on 6 December 2021)), DataONE (https://www.dataone.
org/ (accessed on 6 December 2021)), and GLOBE (Global Learning and Observation to
Benefit the Environment: https://www.globe.gov/ (accessed on 6 December 2021)).
Citizen science has two major advantages:
Remote Sens. 2022,14, 3243. https://doi.org/10.3390/rs14143243 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022,14, 3243 2 of 16
(1) Collecting more data that are potentially more frequent or better distributed geograph-
ically and therefore make it possible to design a monitoring strategy that is better
adapted to the spatial and temporal dynamics of the site.
(2) Increasing awareness of citizens about their environment and contributing to stimulate
scientific curiosity. This aspect is particularly interesting for environmental issues and
risk prevention, but it is not addressed in the present study.
Nevertheless, in some scientific fields, because of the use of consumer-grade sensors
or the low level of expertise of the operators, data collected by citizens always inspire a
certain (sometimes justified) distrust. Where citizen data are of lower quality, they can
nevertheless be useful, either by providing qualitative information or through quantitative
analysis following a different approach from “expert data”. Precisely, considering that
these citizen data are not analyzed in the same way as expert data, it is up to scientists to
develop new processing methods to make the most of these data.
Among the different approaches to citizen science, we can identify, on the one hand,
participation in data processing/analysis [
1
,
2
], e.g., in data annotation for Deep Learning
approaches [
3
,
4
], and on the other hand, data collection, which is of particular interest here.
For data collection, citizen science has already demonstrated its potential for ecological re-
search, particularly for establishing inventories [
5
,
6
]. However, for natural hazards, citizen
participation is often limited to reporting and/or dating hazards or hazard markers [7–9].
In general, data from citizen participation have been used mainly in a qualitative way
(inventories, reporting, etc. [
5
–
9
]), and more rarely in a quantitative way. Moreover, in the
case of quantitative analysis of these data, the requirements are generally lower in terms of
accuracy, due to the “mainstream” nature of the sensors and the limited expertise of the
operators. For example, for the CoastSnap beach monitoring system, which was designed
to capture the coastline dynamics by using compilations of Smartphone photos [
10
], the
root mean square deviations of shoreline position measurements range from 1.4 to 3.9 m
against 0.05 m of accuracy for classical RTK GNSS measurement. Among citizen science
approaches, Structure-from-Motion (SfM) photogrammetry based on crowd-sourced pho-
tographs has the advantage of relying on observation redundancy during photogrammetric
reconstruction and therefore does not require any particular expertise on the part of the
operator, as long as the number and distribution of images are adequate [
11
]. Moreover,
the method is relatively flexible in terms of integrating photos acquired with different
cameras (even short-focal-length Smartphone cameras) in the same reconstruction [
11
]. The
crowd-sourced photogrammetry method has already been used in archaeology, notably
in Cultural Heritage applications [
12
,
13
] for digital preservation or reconstruction of lost
cultural heritage destroyed either by natural disasters or human-related activities.
Without the addition of external constraints, SfM photogrammetry reconstructions are
affected by geometric distortions and/or scaling problems, regardless of the expertise of the
operator. Various studies propose solutions to circumvent these distortion problems. The
solutions can be grouped into two categories: (i) adding constraints during acquisition or
(ii) integrating multi-source data. One commonly used solution is to place ground control
points (GCPs) over the area [
14
–
16
], but unless there are sufficiently remarkable, fixed
points adequately distributed over the study area, this approach is not very applicable to
crowd-sourced photogrammetry.
Other solutions include the study by Monkman et al. [
17
], proposing a methodology
to achieve accurate two-dimensional length estimates of fish with an action camera, using
a background fiducial marker, a foreground fiducial marker, and a laser marker. In the
CoastSnap project to map shoreline position, Harley et al. [
10
] use fixed cradles to constrain
the extrinsic parameters of the cameras. RTK GNSS–assisted SfM photogrammetry [
18
]
also relies on knowing some extrinsic parameters of the camera. In an urban context,
Hartmann et al. [
19
] take advantage of the GNSS information (geotag) contained in the
EXIF of some crowd-sourced images. Griffiths et al. [
13
] complete the crowd-sourced photo
set by adding photographs with scale bars taken by experts. At last, Wu et al. [
20
] carried
Remote Sens. 2022,14, 3243 3 of 16
out 3D reconstructions for urban buildings, using crowd-sourced photos supplemented by
2D building vector data for registration.
The touristic site of the Giant’s Causeway (Northern Ireland, UK) is subject to certain
geohazards, notably rockfalls that put the visitors at risk. Involving visitors in a citizen
science program would both raise awareness on the risks and enable the collection of a
large number of observations. In this study, we assessed the feasibility and quality of
3D reconstructions for geomorphological monitoring of this natural site, using different
crowd-sourced SfM photogrammetry approaches.
2. Methods
2.1. Study Area
The Giant’s Causeway and Causeway Coast WHS is located on the north coast of
County Antrim in Northern Ireland (Figure 1). It was inscribed on the World Heritage List
in 1986 in recognition of its internationally significant geological heritage, but it is also a
site of cultural, historical, and touristic significance with tourism at the site dating back
more than 300 years. Visitors are attracted by the dynamic coastline of rugged cliffs and
40,000 regularly jointed basalt columns. The primary area for visitors, and the focus of this
study, extends 2 km from the Giant’s Causeway visitor center, along a developed path at
the base of the cliffs leading to what is known as the “Grand Causeway”. The path further
extends upslope and diverges between the mid slope sections and another that extends to
the upper path on the headland. The site is characterized by steep-sided scree slopes that
extend from the cliff face to the shoreline.
Remote Sens. 2022, 14, x FOR PEER REVIEW 3 of 17
the extrinsic parameters of the cameras. RTK GNSS–assisted SfM photogrammetry [18]
also relies on knowing some extrinsic parameters of the camera. In an urban context, Hart-
mann et al. [19] take advantage of the GNSS information (geotag) contained in the EXIF
of some crowd-sourced images. Griffiths et al. [13] complete the crowd-sourced photo set
by adding photographs with scale bars taken by experts. At last, Wu et al. [20] carried out
3D reconstructions for urban buildings, using crowd-sourced photos supplemented by 2D
building vector data for registration.
The touristic site of the Giant’s Causeway (Northern Ireland, UK) is subject to certain
geohazards, notably rockfalls that put the visitors at risk. Involving visitors in a citizen
science program would both raise awareness on the risks and enable the collection of a
large number of observations. In this study, we assessed the feasibility and quality of 3D
reconstructions for geomorphological monitoring of this natural site, using different
crowd-sourced SfM photogrammetry approaches.
2. Methods
2.1. Study Area
The Giant’s Causeway and Causeway Coast WHS is located on the north coast of
County Antrim in Northern Ireland (Figure 1). It was inscribed on the World Heritage List
in 1986 in recognition of its internationally significant geological heritage, but it is also a
site of cultural, historical, and touristic significance with tourism at the site dating back
more than 300 years. Visitors are attracted by the dynamic coastline of rugged cliffs and
40,000 regularly jointed basalt columns. The primary area for visitors, and the focus of this
study, extends 2 km from the Giant’s Causeway visitor center, along a developed path at
the base of the cliffs leading to what is known as the “Grand Causeway”. The path further
extends upslope and diverges between the mid slope sections and another that extends to
the upper path on the headland. The site is characterized by steep-sided scree slopes that
extend from the cliff face to the shoreline.
Figure 1. (a,b) Location of the Causeway Coast and Giant’s Causeway in Northern Ireland. (c) Map
of the Causeway Coast, with geological and geomorphological information, and an identification of
actual or potential failure areas (mainly based on Reference [21]). (d) Altitude along the AB profile
in Port Ganny. This study focuses on surveys carried out in Port Ganny, immediately west of the
Grand Causeway (c).
Figure 1.
(
a
,
b
) Location of the Causeway Coast and Giant’s Causeway in Northern Ireland. (
c
) Map
of the Causeway Coast, with geological and geomorphological information, and an identification of
actual or potential failure areas (mainly based on Reference [
21
]). (
d
) Altitude along the AB profile in
Port Ganny. This study focuses on surveys carried out in Port Ganny, immediately west of the Grand
Causeway (c).
2.1.1. Geological Setting
The Giant’s Causeway and Causeway Coast WHS are composed of layers of basalt
that are part of the Antrim Lava Group. This was formed as a result of extensive volcanism
associated with the opening of the North Atlantic Ocean during the Paleogene period,
around 60 million years ago. Volcanic activity continued for about 4 million years in
Remote Sens. 2022,14, 3243 4 of 16
three phases separated by two periods of inactivity [
22
]. The initial phase of volcanic
activity created the Lower Basalt Formation (LBF), which was followed by a hiatus. During
this time, the uppermost flows of the LBF underwent weathering resulting in sequences
of laterite, litghomarge, and bauxite seams known as the Interbasaltic Formation (IBF).
Subsidence associated with magma draining led to the formation of a depression creating a
deep valley which was then infilled with lava as volcanic activity resumed [
23
]. The slow
cooling of this volume of lava led to the formation of the famous regular columnar-jointed
basalts, forming the Causeway Theollite Member (CTM). Another lull in volcanic activity
was followed by further lava extrusions, forming the Upper Basalt Formation (UBF). Today
the site is characterized by the LBF, weathered horizons of the IBF, and CTM visible on the
cliffs, along with high-angled scree slopes.
2.1.2. Site Management
The Giant’s Causeway and Causeway Coast WHS is Northern Ireland’s premier
tourism site, attracting over 1 million visitors every year prior to the COVID-19 pan-
demic [
24
]. However, the Giant’s Causeway has been attracting visitors since long before it
was a tourist attraction and was at the center of a fierce debate disputing the origin of basalt
and other igneous rocks in the 18th century. The basalt columns at the Giant’s Causeway
played a key part in proving that these were, in fact, the product of volcanic activity and,
thus, proving the origin of igneous rocks, making it a site of significant importance in the
development of geological science [25].
The majority of the site is now under the ownership and management of the National
Trust, the UK’s largest conservation charity, with 5% remaining in private ownership and
the marine elements of the site between the high and low water mark being legally owned
by the Crown Estate. A World Heritage Site Steering Group provides the framework for
implementing the site’s management plan, ensuring that conservation and tourism are
carefully balanced. The Steering Group is made up of relevant stakeholders, including the
local council, statutory government agencies, the geological survey, universities, and local
landowners, ensuring that all interests on the site are taken into consideration.
This site is also one of the five observatory pilots deployed by AGEO (Atlantic Geo-
hazard platform) project, which focuses on Geohazard Risk Management.
2.1.3. Geological Hazards
Geological hazards, in the form of slope failures, combined with increasing visitor
numbers and climate change, pose significant challenges to the management of the site.
The area is influenced by regular mass movements in the form of rockfalls, debris
slides, complex landslides, and block falls (Figure 2b). Observations over the past two
decades have identified that the frequency, intensity, and distribution of slope failures
across the site are increasing in response to climatic changes such as increased rainfall and
more frequent and prolonged storm events [
21
]. Over the past decade, the National Trust
has recorded over 300 rockfall and landslide events. This poses a potential significant risk
to visitors who regularly use the lower coastal paths and also the paths that are developed
on the weak scree slope leading to the headland. Much of the slope failures impact the
lower path frequently (Figure 1c) used by visitors and The National Trust staff. The natural
causes of the slope failure are a combination of the geomorphological steep-sided slopes
and cliffs; the variable competency of the geological strata, such as the IBF, which is a
weathered horizon; and the undercutting of cliffs by marine erosion. Rainfall plays a major
role in triggering slope failure, penetrating the stratigraphy through the jointing within the
basalt leading to increased pressure within the slopes and further weathering of the cliff.
Remote Sens. 2022,14, 3243 5 of 16
Remote Sens. 2022, 14, x FOR PEER REVIEW 5 of 17
within the basalt leading to increased pressure within the slopes and further weathering
of the cliff.
Figure 2. (a) Aerial photograph showing Port Ganny. (b) Translational slide at Port Ganny, which
took place during summer 2008 (photo from Reference [21]).
2.2. Site Monitoring by Terrestrial Photogrammetry
Citizen science monitoring requires consideration of the resources to be used to facil-
itate the involvement of volunteers and optimize the quality of their measurements. This
means, for example, training the volunteers on why and how to participate, developing
an application to guide them in their data collection and having an infrastructure that
makes it easy to collect and transfer data. Implementing adequate protocols for citizen
surveys requires prior knowledge for scientists on data acquisition, processing, and anal-
ysis methods, implying a testing phase to account for site-specific constraints. This study
corresponds to the stage of “prototyping” of the monitoring method. Although surveys
were carried out at Port Noffer and in the Port Reostan amphitheater, this article focuses
specifically on the surveys at Port Ganny (Figures 1b and 2a).
2.2.1. “Expert” Reference Dataset
Survey Settings
As the objective is to evaluate the quality of 3D reconstructions integrating crowd-
sourced photos, a first survey was carried out on 22 November 2021 in order to constitute
an “expert reference dataset” serving as benchmark, which was acquired by using RTK
GNSS–assisted SfM photogrammetry [18]. For this survey, a Nikon D800 reflex camera
(20 mm focal length, Nikon, Tokyo, Japan) was used (Figure 3a). A total of 544 photos
were collected, including 204 georeferenced photos from 8 stations with GNSS RTK posi-
tioning of the camera (Figure 3b). The resolution is, on average, 2.2 cm/pixel. The GNSS
antenna was a Leica 1200+ device (Leica Geosystems, Aarau, Switzerland).
It is conceivable that such monitoring could be carried out by trained site-manage-
ment staff.
Figure 2.
(
a
) Aerial photograph showing Port Ganny. (
b
) Translational slide at Port Ganny, which
took place during summer 2008 (photo from Reference [21]).
2.2. Site Monitoring by Terrestrial Photogrammetry
Citizen science monitoring requires consideration of the resources to be used to facili-
tate the involvement of volunteers and optimize the quality of their measurements. This
means, for example, training the volunteers on why and how to participate, developing an
application to guide them in their data collection and having an infrastructure that makes
it easy to collect and transfer data. Implementing adequate protocols for citizen surveys re-
quires prior knowledge for scientists on data acquisition, processing, and analysis methods,
implying a testing phase to account for site-specific constraints. This study corresponds to
the stage of “prototyping” of the monitoring method. Although surveys were carried out
at Port Noffer and in the Port Reostan amphitheater, this article focuses specifically on the
surveys at Port Ganny (Figures 1b and 2a).
2.2.1. “Expert” Reference Dataset
Survey Settings
As the objective is to evaluate the quality of 3D reconstructions integrating crowd-
sourced photos, a first survey was carried out on 22 November 2021 in order to constitute
an “expert reference dataset” serving as benchmark, which was acquired by using RTK
GNSS–assisted SfM photogrammetry [
18
]. For this survey, a Nikon D800 reflex camera
(20 mm focal length, Nikon, Tokyo, Japan) was used (Figure 3a). A total of 544 photos were
collected, including 204 georeferenced photos from 8 stations with GNSS RTK positioning
of the camera (Figure 3b). The resolution is, on average, 2.2 cm/pixel. The GNSS antenna
was a Leica 1200+ device (Leica Geosystems, Aarau, Switzerland).
It is conceivable that such monitoring could be carried out by trained site-management staff.
Data Processing
The acquired photos were processed by SfM photogrammetry, using Agisoft Metashape
®
(Agisoft LLC., St. Petersburg, Russia), according to the following processing chain
(Figure 4a) [18]:
-
Creation and formatting of a camera position file compatible with Agisoft Metashape
®
;
-
Image orientation by bundle adjustment (detection and matching of homologous key
points in overlapping photographs). This step allows us to compute the extrinsic
parameters of each camera.
-
Refinement of camera calibration parameters (intrinsic parameters) by optimization,
using the redundancy of information on pixels observed in several images and the
RTK-georeferenced camera positions. The RTK GNSS–measured positions are taken
as initial values, and their variations are constrained here in a radius of 10 cm.
-
Dense image matching to produce a dense point cloud by using the estimated extrinsic
and intrinsic camera parameters.
Remote Sens. 2022,14, 3243 6 of 16
Remote Sens. 2022, 14, x FOR PEER REVIEW 6 of 17
Figure 3. (a) Measurement system developed for the Real-Time Kinematic (RTK) Global Navigation
Satellite System (GNSS)–assisted terrestrial Structure-from-Motion (SfM) photogrammetry method
(equipped here with the Nikon D800 Reflex camera and the Leica 1200+ GNSS antenna). (b) Posi-
tions of the measured camera stations for the RTK GNSS–assisted photogrammetric survey (PG:
Port Ganny stations).
Data Processing
The acquired photos were processed by SfM photogrammetry, using Agisoft
Metashape® (Agisoft LLC, St. Petersburg, Russia), according to the following processing
chain (Figure 4a) [18]:
- Creation and formatting of a camera position file compatible with Agisoft Metashape®;
- Image orientation by bundle adjustment (detection and matching of homologous key
points in overlapping photographs). This step allows us to compute the extrinsic pa-
rameters of each camera.
- Refinement of camera calibration parameters (intrinsic parameters) by optimization,
using the redundancy of information on pixels observed in several images and the
RTK-georeferenced camera positions. The RTK GNSS–measured positions are taken
as initial values, and their variations are constrained here in a radius of 10 cm.
- Dense image matching to produce a dense point cloud by using the estimated extrin-
sic and intrinsic camera parameters.
With such a method, the reconstruction accuracy is within 5 cm (or lower). The dense
point cloud (about 65 × 106 points) is exported to the open-source software CloudCompare®
(CloudCompare open project, EDF R&D, Paris, France). The point cloud is then filtered
with the SOR (Statistical Outlier Removal) filter proposed by CloudCompare®. It is then
subsampled to 3 cm in order to keep the computation time manageable for the next steps
of this study. To limit edge effects, a common comparison zone is defined (Figure 4b), and
the point cloud is cropped according to this zone and finally meshed. This mesh will be
used as a reference model to assess the quality of reconstructions by using crowd-sourced
pictures.
Figure 3.
(
a
) Measurement system developed for the Real-Time Kinematic (RTK) Global Navigation
Satellite System (GNSS)–assisted terrestrial Structure-from-Motion (SfM) photogrammetry method
(equipped here with the Nikon D800 Reflex camera and the Leica 1200+ GNSS antenna). (
b
) Positions
of the measured camera stations for the RTK GNSS–assisted photogrammetric survey (PG: Port
Ganny stations).
Remote Sens. 2022, 14, x FOR PEER REVIEW 7 of 17
Figure 4. (a) Processing chain for the “expert” dataset used as reference. The SfM photogrammetry
processing is performed by using Agisoft Metashape, and postprocessing steps are performed by us-
ing CloudCompare. (b) Resulting 3D mesh before cropping to a common zone for comparison.
Such a survey is carried out in a few hours and can be operated by the site managers.
The duration of data processing can vary from a few hours to a few days for processing
depending on the power of the computer used (here, it lasted 2 h, using an Intel(R) HD
Graphics 630).
2.2.2. Use of Crowd-Sourced Images
Survey Settings
For this study, the volunteer group was composed of academics (a majority of whom
were not familiar with photogrammetry methods). Once the areas of interest were de-
fined, volunteers were asked to take photos (if possible geotagged, without zoom, and in
landscape mode) with their smartphones. These acquisitions were spread over two days
(22 November 2021 and 25 November 2021) and at different times of the day. Seven dif-
ferent smartphone models were used and are described in Table 1. More than 700 photos
were acquired by the volunteer group.
Table 1. Smartphone devices used during the simulation of crowd-sourced acquisition.
Devices Focal Length (mm) Image Size
CrossCall Core-X4
(Croscall, Aix-en-Pro-
vence, France)
4.71 4000 × 3000
Wiko Y80 V680 3.6 4096 × 2304
Figure 4.
(
a
) Processing chain for the “expert” dataset used as reference. The SfM photogrammetry
processing is performed by using Agisoft Metashape, and postprocessing steps are performed by using
CloudCompare. (b) Resulting 3D mesh before cropping to a common zone for comparison.
With such a method, the reconstruction accuracy is within 5 cm (or lower). The dense
point cloud (about 65
×
10
6
points) is exported to the open-source software CloudCompare
®
(CloudCompare open project, EDF R&D, Paris, France). The point cloud is then filtered
Remote Sens. 2022,14, 3243 7 of 16
with the SOR (Statistical Outlier Removal) filter proposed by CloudCompare
®
. It is then
subsampled to 3 cm in order to keep the computation time manageable for the next steps of
this study. To limit edge effects, a common comparison zone is defined (Figure 4b), and the
point cloud is cropped according to this zone and finally meshed. This mesh will be used as
a reference model to assess the quality of reconstructions by using crowd-sourced pictures.
Such a survey is carried out in a few hours and can be operated by the site managers.
The duration of data processing can vary from a few hours to a few days for processing
depending on the power of the computer used (here, it lasted 2 h, using an Intel(R) HD
Graphics 630).
2.2.2. Use of Crowd-Sourced Images
Survey Settings
For this study, the volunteer group was composed of academics (a majority of whom
were not familiar with photogrammetry methods). Once the areas of interest were de-
fined, volunteers were asked to take photos (if possible geotagged, without zoom, and
in landscape mode) with their smartphones. These acquisitions were spread over two
days
(22 November 2021
and 25 November 2021) and at different times of the day. Seven
different smartphone models were used and are described in Table 1. More than 700 photos
were acquired by the volunteer group.
Table 1. Smartphone devices used during the simulation of crowd-sourced acquisition.
Devices Focal Length
(mm) Image Size
CrossCall Core-X4
(Croscall, Aix-en-Provence, France) 4.71 4000 ×3000
Wiko Y80 V680
(Wiko SAS, Marseille, France) 3.6 4096 ×2304
Huawei POT-LX1
(Huawei Technologies Co., Ltd., Shenzhen, China) 3.6 4160 ×3120
Samsung SM-A125
(Samsung electronics, Seoul, Korea) 4.6 4000 ×3000
Samsung SM-A127
(Samsung electronics, Seoul, Korea) 5.0 4000 ×3000
iPhone XR
(Apple, Cupertino (Californie), USA) 4.25 4032 ×3024
iPhone 5s
(Apple, Cupertino (Californie), USA) 4.0 3264 ×2448
To make the acquisition more realistic, some “inadequate” photos were collected:
pictures outside of the area of interest, blurred images, selfies, photos of people, photos
taken unintentionally, etc. In addition, despite the instructions, some photos were acquired
without geotag (24%), or with a zoom or in portrait mode (4.9%). Among these non-optimal
images, only the photos outside the area of interest are removed (for this pre-filtering step,
the geotag can help if it is present), leaving a total of 684 smartphone photos used in the
tests described below.
Data Processing at a Given Date
Different processing scenarios are proposed to exploit these crowd-sourced images,
some of which also use images from the reference survey (544 photos acquired with the
Nikon D800 reflex camera). The first series of scenarios (Tests 1 to 5) aims to reconstruct
the entire area, while minimizing the proportion of RTK georeferenced images from the
reference survey that need to be included. These different processing scenarios are as
follows (Table 2):
Remote Sens. 2022,14, 3243 8 of 16
-
Test 1: The dataset consists of a subset of the reference dataset, including some
georeferenced and all non-georeferenced photos. The number of RTK stations is
limited here to 5 (PG1, PG2, PG4, PG5, and PG8 stations on Figure 3b).
-
Test 2: The dataset is composed of the georeferenced Nikon camera photos acquired
from the same 5 RTK stations of Test 1 and all the smartphone photos acquired with
the 7 different cameras, both from the foot of the cliff and from the top of the cliff.
-
Test 3: The dataset is once again composed of the georeferenced Nikon-camera photos
acquired from the same 5 RTK stations of Test 1 and all the photos acquired with
the 7 smartphone cameras, but applying a filter on the smartphone photos after
the “Bundle adjustment” step. With this filter, we deactivated the photos whose
alignment error was greater than twice the standard deviation and re-ran the SfM
processing chain.
-
Test 4: For this scenario, only Smartphone photographs are used. All 684 smartphone pho-
tos are used, whether geotagged or not, acquired on different dates (22 November 2021
and 25 November 2021) or acquired from the cliff foot or the cliff top.
-
Test 5: The dataset is, again, composed only of smartphone photographs, geotagged
or not, acquired at different dates, but only those acquired from the cliff foot (the cliff
top being potentially dangerous for citizens).
Table 2. Sets of photographs used in each processing scenario.
Processing Scenario Sets of Photographs Used
Expert reference dataset Georeferenced Nikon D800 photographs (from 8 georeferenced
stations) + non-georeferenced Nikon D800 photographs
Test 1 Georeferenced Nikon D800 photographs (from 5 georeferenced
stations) + non-georeferenced Nikon D800 photographs
Test 2 Georeferenced Nikon D800 photographs (from 5 georeferenced stations) + all smartphone
photographs
Test 3 Georeferenced Nikon D800 photographs (from 5 georeferenced stations) + smartphone
photographs filtered by alignment quality after bundle adjustment
Test 4 All smartphone photographs
Test 5 Smartphone photographs collected from cliff foot
Test 6 (Time-SIFT method)
Smartphone photographs at t1 + dataset of reference at t0 for bundle adjustment step (here,
georeferenced Nikon D800 photographs from 5 georeferenced stations)
For each of these 5 test scenarios, the processing chain is the same (Figure 5a), i.e.,
SfM photogrammetry processing in Agisoft Metashape (similar to that described above) and
postprocessing in CloudCompare. The postprocessing consists of registering the point cloud
with the reference mesh (using the “Iterative Closest Point” method). The point cloud is
then cleaned, first manually, when necessary; then automatically, using the SOR filter; and
then subsampled to 3 cm and cropped to the same area as the reference cloud. Finally, the
point cloud is compared to the reference mesh by calculating distances for all points in the
cloud with a cloud-to-mesh approach.
Data Processing Using the Time-SIFT Method
In the framework of an observatory, one can take advantage of the multi-date aspect
of the datasets, using the Time-SIFT approach for the processing of crowd-sourced data.
The Time-SIFT method, proposed by Feurer and Vinatier [
26
], is based on the properties of
the SIFT (Scale Invariant Feature Transform) algorithm used for image alignment in SfM
processing [
27
]. This Time-SIFT method consists of performing the bundle adjustment step
(to determine the extrinsic and intrinsic parameters of the cameras) by using the largest
possible set of photos, which are then separated into several groups for the computation
of the dense point cloud. The images of the different groups can correspond to photo sets
acquired at different dates, as long as a part of the imaged area remains invariant and
Remote Sens. 2022,14, 3243 9 of 16
recognizable between these different groups. With this method, it is therefore possible to
consider “camera groups” containing fewer images and with less overlap, the density of
information necessary for the correct estimation of camera models being provided by the
superposition of the different groups.
Remote Sens. 2022, 14, x FOR PEER REVIEW 10 of 17
adjustment, these small groups of photos can be aligned with a reference dataset collected
with the RTK GNSS–SfM photogrammetric approach, even if it has not been acquired at
the same date (as long as part of the area remains invariant). Thus, another processing
scenario was set up:
- Test 6: For this test, we are working on a focused area (the screes SW of Port Ganny;
see Figure 4b). The aim is to reconstruct this area by using 330 smartphone photos,
geotagged or not, collected from the foot of the cliff on 25 November 2021. For pro-
cessing in Time-SIFT mode (Figure 5b), these smartphone photos are aligned, during
the bundle adjustment, with 205 additional photos acquired on 22 November 2021,
using the Nikon D800 camera with RTK georeferencing.
Figure 5. (a) Processing chain with «classical» SfM photogrammetry and postprocessing in Cloud-
Compare used for Tests 1 to 5. (b) Processing chain with Time-SIFT SfM processing and postpro-
cessing in CloudCompare used for Test 6.
3. Results
As long as the geometric reliability of the RTK stations network is maintained (sta-
tions spanning a sufficiently large part of the studied area and non-alignment of the sta-
tions to avoid georeferencing ambiguities), the decrease from eight to five RTK stations
(Test 1) has only a very moderate impact on the quality of the reconstruction. Indeed,
when comparing the dense point cloud with the reference mesh, we obtained a mean error
of −0.5 cm and a standard deviation of 4.6 cm (Table 3).
The use of photos from different sensors (here, Nikon SLR camera and the seven
smartphones tested—Table 1) implies different camera settings in terms of sensitivity, ex-
posure, saturation, etc. This diversity of sensors, co mbined w ith va riabl e illu mination con-
ditions, makes the detection of tie-points and, therefore, the image-matching stage more
complicated. This contributes to the appearance of “ghost structures” on the raw point
clouds (Figure 6), which are pseudo-coherent but aberrantly placed reconstructions due
to “sub-networks” of cameras that have not been connected to the main camera network
[19]. These ghosting effects are most pronounced in Tests 2 and 4 (i.e., those involving
shots from the cliff top, hence where the survey configuration favors the appearance of a
Figure 5.
(
a
) Processing chain with «classical» SfM photogrammetry and postprocessing in Cloud-
Compare used for Tests 1 to 5. (
b
) Processing chain with Time-SIFT SfM processing and postprocessing
in CloudCompare used for Test 6.
This approach therefore presents a great potential for crowd-sourced photogrammetry.
Indeed, it seems unrealistic to expect that crowd-sourced photographs will perfectly cover
the whole area with sufficient overlap. Using the Time-SIFT method, it would be possible to
focus on “hotspot areas”, working on smaller groups of photos. During bundle adjustment,
these small groups of photos can be aligned with a reference dataset collected with the
RTK GNSS–SfM photogrammetric approach, even if it has not been acquired at the same
date (as long as part of the area remains invariant). Thus, another processing scenario was
set up:
-
Test 6: For this test, we are working on a focused area (the screes SW of Port Ganny;
see Figure 4b). The aim is to reconstruct this area by using 330 smartphone photos,
geotagged or not, collected from the foot of the cliff on 25 November 2021. For pro-
cessing in Time-SIFT mode (Figure 5b), these smartphone photos are aligned, during
the bundle adjustment, with 205 additional photos acquired on
22 November 2021,
using the Nikon D800 camera with RTK georeferencing.
3. Results
As long as the geometric reliability of the RTK stations network is maintained (stations
spanning a sufficiently large part of the studied area and non-alignment of the stations to
avoid georeferencing ambiguities), the decrease from eight to five RTK stations (Test 1) has
only a very moderate impact on the quality of the reconstruction. Indeed, when comparing
the dense point cloud with the reference mesh, we obtained a mean error of
−
0.5 cm and a
standard deviation of 4.6 cm (Table 3).
Remote Sens. 2022,14, 3243 10 of 16
Table 3.
Cloud-to-mesh distances between the mesh resulting from the “expert dataset” (used as
reference) and the different scenarios testing the incorporation of crowd-sourced photographs.
Test 1 Test 2 Test 3 Test 4 Test 5 Test 6
Mean error −0.5 cm −0.8 cm 0.2 cm 1.7 cm 1.2 cm 0.0 cm
Std. deviation 4.6 cm 32.1 cm 16.9 cm 3.92 m 2.06 m 8.6 cm
The use of photos from different sensors (here, Nikon SLR camera and the seven
smartphones tested—Table 1) implies different camera settings in terms of sensitivity,
exposure, saturation, etc. This diversity of sensors, combined with variable illumination
conditions, makes the detection of tie-points and, therefore, the image-matching stage more
complicated. This contributes to the appearance of “ghost structures” on the raw point
clouds (Figure 6), which are pseudo-coherent but aberrantly placed reconstructions due to
“sub-networks” of cameras that have not been connected to the main camera network [
19
].
These ghosting effects are most pronounced in Tests 2 and 4 (i.e., those involving shots
from the cliff top, hence where the survey configuration favors the appearance of a separate
camera network). To a lesser extent, some limited ghosting effects appeared in Tests 5 and
6. In all cases, these ghost structures are removed manually.
Remote Sens. 2022, 14, x FOR PEER REVIEW 11 of 17
separate camera network). To a lesser extent, some limited ghosting effects appeared in
Tests 5 and 6. In all cases, these ghost structures are removed manually.
Figure 6. Example of «ghosting effect» on the raw dense point cloud generated by Agisoft
Metashape for Test 2.
The comparison of Tests 2, 3, 4, and 5 shows that the addition of photos from a SfM
RTK photogrammetry survey to the crowd-sourced photographs greatly improves (about
ten times better) the quality of the reconstruction (Table 3). Indeed, the standard devia-
tions of Tests 2 and 3 (with georeferenced photos from the reference survey) are, respec-
tively, 32.1 cm and 16.9 cm, against 3.92 m and 2.06 m for Tests 4 and 5 (only with
smartphone photos). Moreover, in both cases, preselecting the images, either according to
their alignment error (Test 3) or simply according to the relevance of the point of view
(here, for example, the foot of the cliff in Test 5), also makes it possible to improve the
accuracy (about twice as good) of the reconstruction compared to processing without pre-
selection (Tests 2 and 4, respectively).
With the Time-SIFT method (Test 6), including reference survey photos (here, georef-
erenced Nikon D800 photographs) in the alignment step and then reconstructing the
dense point cloud only from the crowd-sourced data increases the consistency of each
camera group without reducing the constraints on the estimation of camera parameters.
This approach allows the use of crowd-sourced data in a very satisfactory way in terms of
reconstruction quality, since the standard deviation is 8.6 cm.
Table 3. Cloud-to-mesh distances between the mesh resulting from the “expert dataset” (used as
reference) and the different scenarios testing the incorporation of crowd-sourced photographs.
Test 1 Test 2 Test 3 Test 4 Test 5 Test 6
Mean error −0.5 cm −0.8 cm 0.2 cm 1.7 cm 1.2 cm 0.0 cm
Std. deviation 4.6 cm 32.1 cm 16.9 cm 3.92 m 2.06 m 8.6 cm
Figures 7 and 8 show the spatial distribution of errors. It can be seen that error zones
or non-reconstructed zones are often present in the lower part of the area. This can be
explained by the low angle at which the photographs are taken in relation to the imaged
surface (which is a source of uncertainty in the reconstruction), where the topography
starts to flatten. In addition, it also corresponds to areas with taller vegetation (bushes and
ferns), which makes the photogrammetric reconstruction complicated, especially in
windy conditions. An error zone is also often present at the top right of the area, possibly
due to the presence of small shrubs causing noisier reconstructions.
Figure 6.
Example of «ghosting effect» on the raw dense point cloud generated by Agisoft Metashape
for Test 2.
The comparison of Tests 2, 3, 4, and 5 shows that the addition of photos from a
SfM RTK photogrammetry survey to the crowd-sourced photographs greatly improves
(about ten times better) the quality of the reconstruction (Table 3). Indeed, the standard
deviations of Tests 2 and 3 (with georeferenced photos from the reference survey) are,
respectively, 32.1 cm and 16.9 cm, against 3.92 m and 2.06 m for Tests 4 and 5 (only with
smartphone photos). Moreover, in both cases, preselecting the images, either according to
their alignment error (Test 3) or simply according to the relevance of the point of view (here,
for example, the foot of the cliff in Test 5), also makes it possible to improve the accuracy
(about twice as good) of the reconstruction compared to processing without preselection
(Tests 2 and 4, respectively).
With the Time-SIFT method (Test 6), including reference survey photos (here, geo-
referenced Nikon D800 photographs) in the alignment step and then reconstructing the
dense point cloud only from the crowd-sourced data increases the consistency of each
camera group without reducing the constraints on the estimation of camera parameters.
This approach allows the use of crowd-sourced data in a very satisfactory way in terms of
reconstruction quality, since the standard deviation is 8.6 cm.
Remote Sens. 2022,14, 3243 11 of 16
Figures 7and 8show the spatial distribution of errors. It can be seen that error zones
or non-reconstructed zones are often present in the lower part of the area. This can be
explained by the low angle at which the photographs are taken in relation to the imaged
surface (which is a source of uncertainty in the reconstruction), where the topography
starts to flatten. In addition, it also corresponds to areas with taller vegetation (bushes and
ferns), which makes the photogrammetric reconstruction complicated, especially in windy
conditions. An error zone is also often present at the top right of the area, possibly due to
the presence of small shrubs causing noisier reconstructions.
Remote Sens. 2022, 14, x FOR PEER REVIEW 12 of 17
Figure 7. Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the different scenarios testing the incorporation of crowd-
sourced photographs (illustrations (a–e) corresponding, respectively, to Tests 1 to 5). Note: The color
scale varies between the different tests.
Figure 8. Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the point cloud corresponding to the area reconstructed by
using Time-SIFT SfM photogrammetry.
Figure 7.
Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the different scenarios testing the incorporation of crowd-
sourced photographs (illustrations (
a
–
e
) corresponding, respectively, to Tests 1 to 5). Note: The color
scale varies between the different tests.
Remote Sens. 2022, 14, x FOR PEER REVIEW 12 of 17
Figure 7. Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the different scenarios testing the incorporation of crowd-
sourced photographs (illustrations (a–e) corresponding, respectively, to Tests 1 to 5). Note: The color
scale varies between the different tests.
Figure 8. Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the point cloud corresponding to the area reconstructed by
using Time-SIFT SfM photogrammetry.
Figure 8.
Spatial distribution of the cloud-to-mesh distances between the mesh resulting from the
“expert dataset” (used as reference) and the point cloud corresponding to the area reconstructed by
using Time-SIFT SfM photogrammetry.
Remote Sens. 2022,14, 3243 12 of 16
4. Discussion
4.1. Data Quality in Citizen Science
As citizens may lack scientific expertise or technical skills, citizen data quality is
widely discussed in the literature (see, for example, References [
28
–
30
]). Nevertheless,
some studies show that a variety of citizen science projects have produced data with quality
equal to or higher than the dataset collected by professionals [29].
It depends on what is meant by “quality” (accuracy of the measurement, distribution
of measurements, sufficient sample size, etc.). In this case of geomorphological monitoring,
the quality threshold depends on the amplitude of the signal to be detected. Here, we are
interested in landslides or rockfalls corresponding to distance between pre- and post-event
surfaces in the order of 10 cm to 10 m.
The residual errors after registration with the reference reconstruction (measured here
by the standard deviation) correspond to distortion or reconstruction problems (for large
errors of the order of several meters) or noise (for errors of a few centimeters to a few tens of
centimeters). As mentioned in Reference [
31
], the high standard of quality that is achieved
quickly, simply, and at a low cost by using crowd-sourced photogrammetry combined with
the Time-SIFT method is more than suitable for characterizing most geohazards.
One of the advantages of crowd-sourced photogrammetry is that, from the citizen’s
point of view, it is a participatory science activity without protocol. This is particularly
interesting when working with tourists, who typically, at least for non-national visitors,
only come to the site once, so that one cannot expect them to participate if they need to be
familiar with a complex protocol. Here, as everyone knows how to take a picture, the only
criterion to be considered is the relevance of the photographed area. As long as the area
of interest is visible in enough images and from enough distinct points of view (the more
participants there are, the easier these criteria are fulfilled), the dataset is not affected by the
scientific or technical skills of the “operators”. Indeed, the quality of the 3D reconstruction
depends mainly on the processing strategy.
Moreover, in the case of a very large number of participants, in order to limit processing
times, it is possible to pre-filter the photos according to their relevance, for example, by
removing redundant photos on the over-photographed areas.
4.2. Technical Suggestions for Improving the Method
In practice, limiting the number of RTK stations enables reducing the survey time for
site management staff. Alternatively, fixed pivoting stations could be set up to serve as
Smartphone supports, which would make it possible to do without the RTK GNSS–assisted
SfM photogrammetry frame and tripod.
In order to limit the ghosting effects, it might be possible to develop a routine to
equalize the colorimetry of the images beforehand. Since the photos transferred by citizens
are generally in JPEG format, care should be taken to ensure that these manipulations do not
affect the quality of the images and interfere with the SfM photogrammetry reconstruction.
One of the important points for the implementation of a long-term monitoring pro-
gram by using crowd-sourced photogrammetry is to determine the frequency of SfM RTK
photogrammetry reference surveys, as well as the relevant frequency to group the crowd-
sourced images for the Time-SIFT processing method. These criteria depend both on the
rate of morphological evolution (the frequency of occurrence of mass movements induced
by the major hazards (rockfalls, landslides, etc.) and on the seasonal evolution of the site
(e.g., evolution of the color and size of the vegetation). In the event of a major change
in the morphology of the site, it is recommended that a new reference survey be carried
out. Time-SIFT photo groups should be set up in such a way that the imaged areas are
morphologically stable over the period of acquisition of photos within each group and
that the images in the same group have a consistent appearance (e.g., no morphological
evolution or color change within the same group).
The development of a smartphone app dedicated to the acquisition of these crowd-
sourced photos would make it possible to better constrain certain image acquisition recom-
Remote Sens. 2022,14, 3243 13 of 16
mendations, e.g., take photos in landscape mode to maximize coverage and not have to
deal with different orientations, prevent zooming, ensure that the images have a geotag,
etc. This application could also allow citizens to transfer their photographs more easily,
either via a 4G connection, free Wi-Fi hotspot, or via Bluetooth to a terminal dedicated
for data collection that would be installed on the site, for instance, near the visitor center.
With geotagged images, the app could even use citizen photographs that have already
been acquired to create heat maps of photographed areas and guide citizens in choosing
which areas to photograph while they are taking photographs. The creation of heat maps of
photographed areas could also help to identify over-photographed areas, which is where
some photos could be removed at the stage of data processing to reduce processing times.
4.3. Interactions with Citizens
Involving citizens in a photogrammetric survey requires thinking of a way to give
some explanations beforehand: Why is it important to monitor this site? What will the
data collected be used for? What is the best way to collect the data? Apart from using a
smartphone app, on a tourist site such as the Giant’s Causeway, there are several ways of
informing potential participants: explanatory panels at the visitor center or at the entrance
to the site, information on audio guides, information directly transmitted via the site
management staff, etc. In particular, citizens must be made aware of the fact that they
should not put themselves in danger in order to collect images (by approaching a cliff edge
or a rockfall, for example). The results of the project could also be advertised through a
website, at the visitor center with virtual exhibitions, or through the mobile app, using
Augmented Reality, as it has been performed in the HeritageTogether project [32]
Although many studies (e.g., Reference [
33
]) point out the long-term commitment of
citizens as a major challenge, working on a tourist site is an asset. Indeed, as the target
audience mainly consists of tourists, the question of maintaining citizen engagement is
not so critical. Nevertheless, citizens are more likely to participate if they receive feedback.
Here, the information regarding the monitoring program that would be provided at an
exhibit in the visitor center or on a dedicated app or website could also show prior results,
using participatory data.
While adding the photographs to a heat map as the images are transferred would use
computing resources (the cost-benefit ratio would need to be assessed by also considering
the associated carbon footprint), using the geotag information to overlay on a map the
locations from which the pictures were taken is more straightforward. In order to further
promote citizen participation and even to guide citizens, the image acquisition could be
thought of as a playful activity, such as Pokemon Go (https://pokemongolive.com/en/
(accessed on 16 March 2022)).
4.4. Integration of These Results into an Observatory Strategy
The opportunity of monitoring the 3D morphological evolutions over time is an asset
for the management of the site. Indeed, in addition to the volume balances and the more
precise assessment of erosion rates and their variability in space and in time, the time series
of 3D reconstructions also allows a more complete vision (notably spatially) of the hazard
markers and thus a better understanding of the processes at play. It is possible to integrate
these data into a multi-source monitoring approach by coupling them with geotechnical
and hydrological measurements, for example, in order to have a more complete panel of
measured variables and thus a better anticipation of events.
To complement these surveys and better identify the dates of hazards (and thus the
relevant periods for processing crowd-sourced data), video monitoring could be considered.
Indeed, even if they do not allow us to produce 3D models, surveillance cameras are widely
used to better constrain the temporal component of environmental observations, with low
costs and human efforts (see References [
34
–
36
]). However, it implies that a system (video
sensor, data supply, and transfer modes) that is compatible with the site constraints should
be designed. Their use also requires the identification of a suitable location (viewpoint
Remote Sens. 2022,14, 3243 14 of 16
and distance from the monitored area), which also needs to be non-disruptive in this
preserved area.
In the framework of a geo-hazard observatory and of a long-term monitoring strategy,
in order to convey the results derived from this citizen science dataset in a way that can be
understood by all the stakeholders involved in management (including the citizens poten-
tially contributing to the acquisition of photographs), hazard “indicators” can be defined
to serve both as evaluation and decision-making tools. These indicators are a simplified
representation of the measured variables, according to a postprocessing method typically
involving formulae combining several variables, spatial interpolation, and discretization
into classes. Since the use of indicators implies a certain “smoothing” of the measurement
results, there is a higher tolerance to measurement errors, which is well-suited for crowd-
sourced photogrammetric monitoring. In order to better reflect the sensitivity of the data
(the capacity to detect and discriminate mass movements of different types, at different
scales, and related to different processes), indicators can be derived on a high-resolution
grid since SfM photogrammetry provides dense point clouds.
5. Conclusions
The Giant’s Causeway is a very attractive site for tourists, but the cliffs bordering the
site present a significant risk of mass movements and falling rocks. The site is therefore
subject to extensive surveillance by the stakeholders. Given that implementing in situ
instruments on such a site is complicated, it appears interesting to combine in a crowd-
sourced photogrammetry approach the possibilities of in situ remote sensing methods
and the opportunity of the number of potential operators considering all the visitors.
Furthermore, the involvement of citizens through a participatory science program appears
to be a real opportunity to educate them about geohazards and, more broadly, about the
observation of the environment.
The tests demonstrate that combining crowd-sourced photographs and the Time-SIFT
processing approach (based on expert surveys) allows us to provide 3D reconstructions
with precision within 10 cm (here a standard deviation of 8.6 cm). Although some practical
implementation issues remain to be resolved, complementing reference RTK GNSS–assisted
terrestrial SfM photogrammetry surveys with crowd-sourced photogrammetry surveys
offers a great potential. Indeed, it becomes possible to space out the reference surveys while
collecting, thanks to citizens, more frequent measurements that are better distributed in
time, and therefore allowing better dating of events or changes. Thus, it will be easier to
target the link between the event and the triggering processes to improve the understanding
of the mechanisms and thus improve the anticipation of hazards.
Author Contributions:
Conceptualization, M.J.; methodology, M.J.; software, M.J.; validation, M.J.
and N.L.D.; formal analysis, M.J.; investigation, M.J., N.L.D., K.P., K.L. and S.L.; resources, M.J.,
N.L.D. and K.P.; data curation, M.J.; writing—original draft preparation, M.J., N.L.D., K.P., K.L. and
S.L.; writing—review and editing, M.J. and N.L.D.; visualization, M.J.; supervision, R.C.G. and C.D.;
project administration, R.C.G., N.L.D., K.P. and K.L.; funding acquisition, R.C.G. and C.D. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement:
The reference dataset (3D point cloud) is available on the Indigéo
portal: https://doi.org/10.35110/2774795b-e223-47c7-82d4-03f2b2ceecef.
Acknowledgments:
The authors acknowledge financial support provided by the European program
Interreg Atlantic Area via the project AGEO (EAPA 884/2018). The authors would also like to thank
the National Trust for providing access to the sites.
Conflicts of Interest: The authors declare no conflict of interest.
Remote Sens. 2022,14, 3243 15 of 16
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