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Journal of Geovisualization and Spatial Analysis (2024) 8:15
https://doi.org/10.1007/s41651-024-00179-z
Improving Urban Mapping Accuracy: Investigating theRole ofData
Acquisition Methods andSfM Processing Modes inUAS‑Based Survey
Through Explainable AI Metrics
LorándAttilaNagy1 · SzilárdSzabó1 · PéterBurai2 · LászlóBertalan1
Accepted: 29 April 2024 / Published online: 9 May 2024
© The Author(s) 2024
Abstract
In this study, we investigated the accuracy of surface models and orthophoto mosaics generated from images acquired using
different data acquisition methods at different processing levels in two urban study areas with different characteristics.
Experimental investigations employed single- and double-grid flight directions with nadir and tilted (60°) camera angles,
alongside the Perimeter 3D method. Three processing levels (low, medium, and high) were applied using SfM software,
resulting in 42 models. Ground truth data from RTK GNSS points and aerial LiDAR surveys were used to assess horizontal
and vertical accuracies. For the horizontal accuracy test, neither the oblique camera angle nor the double grid resulted in an
improvement in accuracy. In contrast, when examining the vertical accuracy, it was concluded that for several processing
levels, the tilted camera angle yielded better results, and in these cases, the double grid also improved accuracy. Feature
importance analysis revealed that, among the four variables, the data acquisition method was the most important factor
affecting accuracy in two out of three cases.
Keywords UAS-SfM· Accuracy assessment· Data acquisition types· Oblique imaging· Flight directions
Abbreviations
DSM Digital surface model
GCP Ground control point
HPL High processing level
MPL Medium processing level
LPL Low processing level
NG1 East-west nadir
NG2 North-south nadir
NG3 Nadir double grid
OG1 East-west oblique
OG1 East-west oblique
OG2 North-south oblique
OG3 Oblique double grid
P3D Perimeter 3D
RMSE Root mean square error
SfM Structure from motion
UAS Unmanned aerial system
Introduction
Currently, UASs play an increasingly pivotal role in the
realm of spatial data collection. Unlike conventional air
and satellite-based remote sensing methods, the UAS offers
significant advantages in facilitating data acquisition and
adapting to the specific requirements of different survey
studies with a variety of spatial and temporal resolutions.
This versatility is further underscored by the utilization of
a broad spectrum of sensor types, all of which are achieved
at a comparatively low cost (Yao etal. 2019; Iglhaut etal.
2019; Nex etal. 2022). The SfM method is a key solution for
processing aerial imagery obtained by UASs. Notably, the
parameters are integral to the processing workflow, includ-
ing camera calibration, bundle adjustment, and orientation
parameter adjustments, all of which are implemented in a
user-friendly manner (Uysal etal. 2015; Iglhaut etal. 2019;
Deliry and Avdan 2021).
The increasing adoption of UAS-based SfM techniques
for mapping and modeling is evident from its growing
* Loránd Attila Nagy
nagy.lorand@science.unideb.hu
1 Department ofPhysical Geography andGeoinformatics,
University ofDebrecen, H-4032 Egyetem Tér 1, Debrecen,
Hungary
2 Remote Sensing Centre, University ofDebrecen,
Böszörményi Út 138, 4032Debrecen, Hungary
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 2 of 19
utilization across various domains, including agriculture,
environmental studies (Akay etal. 2022; Dai etal. 2022),
and industrial applications (Haas etal. 2016; Rauhala etal.
2017), as well as urban environments (Ahmed etal. 2021).
In contrast to previous methods dominated by aerial and
terrestrial laser scanning for high-resolution topographic
data production, UAS SfM demonstrates superior efficiency
through its capability for flexible, target-oriented point cloud
generation at resolutions approaching sub-decimeter scales,
thereby facilitating more intricate analyses (Manfreda and
Eyal 2023).
The efficacy of UAS SfM compared with laser scanning
has been substantiated in various contexts. For instance, a
comparison of the accuracy of the UAS-based SfM and the
LiDAR digital surface model (DSM) in an urban area for
flood estimation yielded inconsequential disparities (Escobar
Villanueva etal. 2019). Studies also investigated the feasibil-
ity of employing the UAS-based SfM for pre-disaster condi-
tion assessment, determining that its vertical error tolerance
(sub-decimeter) qualifies it for comparative analyses before
and after disasters (Kucharczyk and Hugenholtz 2019). Fur-
thermore, the method’s morphometric accuracy was evalu-
ated alongside a terrestrial laser scanner (TLS), particularly
for documenting historic districts (Parrinello and Picchio
2019). Additionally, UAS-based aerial mapping has proven
invaluable in generating foundational data for estimating the
roof area of agricultural structures, facilitating the efficient
planning of solar panel installations (Koc etal. 2020). In
built-up areas, Liu etal. (2022a) investigated the effects of
UAS flight altitude and image overlap degrees, while Ahmed
etal. (2022) explored the influence of varying camera angles
and flight directions on the accuracy of UAS-derived out-
puts, namely, DSM and orthophoto mosaics.
The accuracy of the method must be rigorously assessed
to ensure the efficiency and reliability of UAS SfM-based
data. This entails quantification of the horizontal errors
inherent in the produced orthophoto mosaics and vertical
errors within the DSMs. The exigency for such assessments
is grounded in the profound impact of these parameters on
the overall uncertainty associated with the generated models
(Inzerillo etal. 2018; Belcore etal. 2021). Various factors
pertaining to UAS-based SfMs can influence the reliability
of generated models. These include properties associated
with direct or indirect external orientation parameters, such
as the scale of GNSS measurement errors, the number and
spatial distribution of ground control points (GCPs), cam-
era characteristics (e.g., tilt of the camera principal axis,
focal length, lens distortions), diverse weather conditions
(e.g., lighting and atmospheric properties), and the quality
of the resultant images (e.g., sharpness, resolution, blur). In
addition, the processing software environment significantly
contributes to the quality of the derived datasets (Barkóczi
and Szabó 2017; Sanz-Ablanedo etal. 2018; Lehoczky and
Siki 2020; Casella etal. 2020).
The significance of external orientation has been promi-
nently underscored by numerous studies dedicated to reveal-
ing its details and effects. These investigations have col-
lectively determined that an optimal spatial distribution of
GCPs is achieved through their homogeneous and nonlinear
dispersion within the study area (Manfreda etal. 2019; Vil-
lanueva and Blanco 2019; Gomes Pessoa etal. 2021; Ulvi
2021). The establishment of universally accepted princi-
ples dictating the minimum and optimal quantity of GCPs
remains elusive, as studies employ varied benchmarking sys-
tems. For instance, Sanz-Ablanedo etal. (2018) investigated
the influence of the number of GCPs per image on accuracy,
revealing that beyond 3–3.5 GCPs per 100 images, no statis-
tically significant improvement in accuracy was observed.
Conversely, contrasting findings have been reported, indicat-
ing that there is no statistically significant enhancement in
accuracy when the number of GCPs surpasses 10 per square
kilometer (Liu etal. 2022a, b). In addition to the size of the
area under consideration, the topography of the site mark-
edly influences the required number of GCPs. Consequently,
a site characterized by heterogeneous topography, with an
average slope of 19–30%, may necessitate a higher number
of GCPs (Agüera-Vega etal. 2017), in contrast to a homoge-
neous flat area (Reshetyuk and Mårtensson 2016).
An increasing number of investigations have emphasized
the benefits associated with utilizing the oblique data acqui-
sition method for evaluating UAS-based aerial data (Li etal.
2018; Wu etal. 2018; Vetrivel etal. 2018). The primary
justification for this preference stems from the unique capa-
bility of oblique data acquisition to capture detailed informa-
tion not only from the top but also from the sides of objects
(Svennevig etal. 2015; Jaud etal. 2019; Cao etal. 2023),
contrasting with nadir imaging. Its application is favored in
scenarios where vertical or near-vertical surfaces dominate
the study area. Research dedicated to quarry wall surveys
has demonstrated that integrating nadir and oblique imagery
improves the accuracy of DSMs while simultaneously
addressing data gaps caused by shadowing effects (Rossi
etal. 2017; Zapico etal. 2021). Similar results have been
noted in investigations carried out in naturally high-relief
regions (Nesbit and Hugenholtz 2019; Bi etal. 2021; Nesbit
etal. 2022). The enhanced accuracy and heightened infor-
mational content evident in models created by integrating
nadir and oblique images support the utility of this approach
in computing the leaf area index (LAI) (Che etal. 2020;
Lin etal. 2021). Furthermore, its progressive deployment
in urban settings is attributed to its ability to provide ade-
quate data on building frontages, which can be harnessed for
precise generation of 3D building models (Verykokou and
Ioannidis 2016; Wu etal. 2018; Pepe and Costantino 2020).
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 3 of 19 15
In addition to the aforementioned factors, subsequent
investigations into the evaluation of UAS-SfM methods have
focused on additional categories of variables that influence
accuracy. These include parameters such as flight altitude
and image overlap (Jeong etal. 2020; Anders etal. 2020; Liu
etal. 2022b). However, the novelty of our research lies in (1)
its comprehensive comparison of three distinct factors influ-
encing accuracy, (2) the comparison of two different built-in
study areas with different characteristics, and (3) the utiliza-
tion of three reference groups, one horizontal and two verti-
cal. Our study is unique in its simultaneous examination of
these variables, which have not been studied together in this
manner previously. This holistic approach allows for a more
nuanced understanding of the interplay between various
factors affecting accuracy in UAS-based surveys, particu-
larly in urban environments. Furthermore, our investigation
highlights the impact of processing software settings and
duration on model quality and processing time, aspects often
overlooked in accuracy studies. By addressing these gaps,
our research not only advances the current understanding of
UAS-based survey methodologies but also provides valuable
insights for optimizing aerial mapping campaigns in urban
settings. Considering our results, we aimed to underscore
the universal limitations in applying the investigated survey
conditions to enhance the general efficacy of urban aerial
mapping campaigns with UASs.
Materials andMethods
Study Area
This research was conducted in two distinct study areas
within Debrecen, Hungary, as illustrated in Fig.1. The first
area, designated as “Csapókert,” is situated in the north-
eastern part of the city. It is characterized by a regular street
network, expansive gardens, lower building density, and pre-
dominantly one- or two-floored structures. The second study
area, “Úrrétje,” is in the north-western part of the city. This
area features a denser and more irregular road network with
smaller gardens and increased building density. It includes
multistory blocks of flats, as well as terraced and detached
Fig. 1 Study area
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 4 of 19
houses. In contrast to Csapókert, Úrrétje exhibits a homo-
geneous flat terrain. Despite Debrecen’s overall flat land-
scape, there were disparities in the topography of the two
study areas. Csapókert comprises two sand mounds ranging
in height from 1.5 to 2m, contributing to its topographical
variation. On the other hand, Úrrétje represents a consist-
ently flat terrain. The selection of these two areas was inten-
tional and based on prior research findings (Kucharczyk and
Hugenholtz 2019), which suggested that varying character-
istics, such as building height, built-in area density, and spa-
tial patterns, may influence the accuracy of both orthophoto
mosaics and DSMs.
UAS Data Acquisition
Image acquisition was executed using a DJI Mavic Pro quad-
copter, equipped with an integrated 12-megapixel 1/2.3″
sensor featuring a focal length of 26mm. The camera is
fixed to the drone through a 3-axis gimbal, enabling the
adjustment of the camera tilt along the X-axis from − 90°
(nadir) to + 30°. Aerial surveys were conducted in the two
designated study areas at almost simultaneous times, with
merely a 5-day difference between the two assessments. To
ensure comparability, both study areas were surveyed during
the same time of day (early afternoon), and strict consistency
was maintained in the flight parameters, encompassing vari-
ous aspects. Throughout the entirety of the data collection
process, a consistent flight altitude of 90m above ground
level was maintained, with a frontlap and sidelap of imagery
set at 70%. Image acquisitions were performed using three
different flight modes (Fig.2):
- Single grid: the UAS flies over the area to be mapped in
a series of parallel paths in a defined direction.
- Double grid: at the end of the initial flight pass, the
UAS flies along a series of passes but perpendicular to
the previous one.
- Perimeter 3D: the UAS flew a single-grid plan and a
square bounding the area to be mapped, the former with
a camera angle of 90° (nadir) and the latter with a camera
angle positioned at the spatial center of the area (Fig.2).
For data collection, two single-grid flights were imple-
mented, one conducted in an east–west (E-W) flight direc-
tion and the other in a north–south (N-S) flight direction,
in addition to a double-grid flight pattern. All three flight
modes were executed with a 90° nadir and a 60° oblique
camera angle. The number of photos captured for each data
acquisition method is listed in Table1. To establish accu-
rate external orientation parameters, the aerial data collec-
tion process was complemented with GCP surveys. Prior
to aerial data collection, the GCPs were established by
positioning the signs along the roadway. The coordinates of
these GCPs were surveyed using a Stonex S9i RTK GNSS
system, which demonstrated an average positioning error of
approximately ± 2cm. In total, 23 GCPs were distributed
for the Úrrétje area, while 25 GCPs were designated for the
Csapókert area (Fig.3).
Fig. 2 The applied data acquisition methods
Table 1 The numbers of
the images of the different
acquisition methods
Csapókert Úrrétje
NG1 144 189
NG2 141 180
NG3 285 369
OG1 144 189
OG2 140 180
OG3 284 369
P3D 229 258
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Reference Data
To evaluate the accuracy of the orthophoto mosaics and
DSMs produced via SfM processing, two distinct types of
reference data were utilized. In both study areas, an RTK
GNSS device (Stonex S9i) was used to survey the GCPs
and collect reference coordinates, as previously described.
Given the nature of the areas, which are characterized by
a high number of enclosed gardens, reference points were
surveyed exclusively in public areas, typically on roadways.
These points were employed to evaluate both the horizontal
and vertical accuracies of the resulting orthophoto mosaics
and DSMs. A uniform sampling approach was adopted for
the analysis, involving the measurement of 100 points in
each of the two study areas (Fig.4). Additionally, to assess
the vertical data accuracy, a LiDAR point cloud, acquired
from a survey conducted using a RIEGL VQ-780II sensor,
was utilized. Airborne laser scanning (ALS) data collection
occurred 2months after the UAS-based survey, with a flight
altitude of 1078m above ground level and an average point
density of 16 points per square meter. DSMs were generated
for both study areas at a spatial resolution of 0.25m per pixel
using LiDAR point cloud data. For this type of reference,
100 points were randomly generated for each area (Fig.4).
These points were then used to extract the height values
from the rooftops of the buildings.
Data Processing
UAS-based aerial imagery was processed using the Agisoft
Metashape Professional (v1.5.1) software environment.
In our SfM processing workflow using Metashape, four
distinct stages are included, which encompass the following
procedure:
I) During the Align Photos stage, the software identifies
tie points to establish connections between different
images, thereby creating a block for subsequent block
adjustments. The outcome of this process is a sparse
point cloud with default software settings. Follow-
ing this, georeferencing of the models was conducted
using GCPs.
Fig. 3 The distribution of GCPs in the two areas
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 6 of 19
II) A dense point cloud was created using the built-in
dense point cloud option. This involved employing
low, medium, and high processing modes for vari-
ous types of acquisitions, with aggressive depth fil-
tering applied in all cases. The processing modes
correspond to different image resolutions, with the
software calculating 1/8 of the pixels for low,1/4 for
medium, and 1/2 for high.
III) The generation of DSMs followed, utilizing the dense
point cloud data with interpolation enabled, and the
default resolution specified by the software according
to the ground sampling distance characteristics of the
lens and image acquisition.
IV) Finally, orthophotos were generated using the previ-
ously derived DSMs. The mosaic blending mode was
employed, and the resolution was determined using
software.
SfM processing was performed on a dedicated Work-
station PC with an Intel Xeon E5-2670 CPU (12/24 cores/
thread), 64GB DDR5 RAM memory, and a Nvidia Quadro
K420 GPU (2GB VRAM) configuration.
Accuracy Assessment
The horizontal accuracies of the orthophoto mosaics were
determined by calculating the absolute distances between
the reference and model coordinates along the X- and Y-axes
using trigonometry. These distances were treated as abso-
lute (i.e., positive) values and did not incorporate directional
information. For the DSMs, the disparities between the refer-
ence data (elevation data obtained from GPS measurements
for roads and extracted elevation values from LiDAR DSM
for roofs) and elevation values derived from the generated
models were assessed.
Initially, the obtained difference values were analyzed
using standard descriptive statistical methods. The RMSE
was subsequently determined for both vertical and horizontal
tests by evaluating the differences between the reference and
model data.
Fig. 4 The position of the two types of references in the two areas
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 7 of 19 15
Modeling withResiduals
The residuals, which denote the disparities between the SfM
models and the reference date, were subjected to evaluation
using random forest regression. In this analysis, the depend-
ent variable was the residual values, while the independ-
ent variables included the resolution (level), flight pattern
(image acquisition), flight direction (direction), and the
study area. The reference data were divided into training
and testing datasets at a 70:30 ratio, and the models were
trained using the training set. The modeled values for the
testing data were subsequently calculated, and the minimum,
median, and maximum values were selected for further anal-
ysis by employing explainable machine learning techniques.
The models were evaluated using explainable AI met-
rics. The rank of the feature importance was determined by
performing 50 permutations, and the contribution of the
independent variables was evaluated using breakdown plots.
These plots provide a visual representation of the variables’
contributions to a specific value of the dependent variable,
indicating both positive and negative contributions. The cal-
culations were carried out using R 4.2.2 (R Core Team) with
DALEX and the forester packages.
The whole data collection, processing, and analysis pro-
cess is shown in Fig.5.
Results
Horizontal Accuracy
Comparing the error ranges between the two study areas
using the same method and processing level, it was observed
that in 19 modeling cases, the Úrrétje area exhibited smaller
differences. However, for the Csapókert area, only the MPL
(0.23m) and LPL (0.37m) processing levels of the P3D
method demonstrated a better performance (Fig.6). In terms
of the RMSE values for distances, it was noted that only
the P3D LPL mode yielded better results (0.5cm) for the
Csapókert area compared to the Úrrétje area. Importantly,
Fig. 5 Flow chart of the steps performed during the study
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 8 of 19
for the same processing mode and level between the two
study areas, the discrepancy in the RMSE values was less
than 2cm.
The results demonstrated consistent patterns when com-
paring data collection methods in both study areas. Specifi-
cally, at the same processing level, the NG1 method con-
sistently yielded the smallest error range, whereas the P3D
method consistently yielded the largest error range. Cor-
respondingly, for RMSE values, the NG1 method consist-
ently provided the most accurate results, whereas the P3D
method resulted in the least accurate outcomes (Table2).
However, when specifically considering RMSE values for
oblique images, it was observed that the double-grid flight
employed for both study areas outperformed the single-grid
flight at all three processing levels, yielding better overall
results.
Comparing the three processing levels examined when
looking at the range values for both study areas, there were
pairs of results: for example, in the NG1 processing Úrré-
tje area and in the OG2 method’s Csapókert. In the inves-
tigation of RMSE values, there were no cases in which
lower processing levels resulted in better accuracy of the
Úrrétje results. In addition, for Csapókert, only the OG3
method had a 0.2cm higher MPL than the HPL.
According to the feature importance, the image acqui-
sition method (0.0303) had the greatest impact, whereas
the processing level (0.0298) had the least impact on the
error rates. Except for the acquisition mode, the other three
features were observed with minimal differences (Fig.7).
Breakdown analysis revealed that the Csapókert area and
the oblique acquisition mode produced the best results
with the smallest error.
The processing level (medium) did not significantly
affect the error, whereas the double-grid method contrib-
uted to the error reduction. In terms of the average error
value, the error was reduced by factors such as the Úrrétje
area, camera angle, and N-S flight direction. Conversely,
a low processing level increases error. Regarding the
maximum error, the Úrrétje location contributed to error
reduction, whereas the P3D method, low processing level,
and the associated flight direction with P3D increased the
error.
Fig. 6 The result of the horizontal accuracy assessment
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 9 of 19 15
Table 2 Values in meters of the
median, range, and RMSE of
the horizontal precision test
Acquisition Processing level Median Range RMSE
Csapókert Úrrétje Csapókert Úrrétje Csapókert Úrrétje
NG1 HPL 0.033 0.026 0.045 0.032 0.097 0.091
NG1 MPL 0.038 0.025 0.049 0.033 0.102 0.088
NG1 LPL 0.042 0.027 0.052 0.034 0.107 0.091
NG2 HPL 0.044 0.033 0.057 0.041 0.128 0.117
NG2 MPL 0.045 0.040 0.061 0.045 0.142 0.112
NG2 LPL 0.049 0.039 0.068 0.044 0.161 0.116
NG3 HPL 0.042 0.041 0.058 0.045 0.132 0.111
NG3 MPL 0.042 0.040 0.058 0.046 0.132 0.124
NG3 LPL 0.043 0.041 0.064 0.049 0.143 0.127
OG1 HPL 0.044 0.037 0.060 0.045 0.135 0.112
OG1 MPL 0.054 0.038 0.064 0.048 0.149 0.104
OG1 LPL 0.056 0.046 0.067 0.057 0.125 0.119
OG2 HPL 0.047 0.035 0.061 0.045 0.154 0.104
OG2 MPL 0.053 0.036 0.065 0.046 0.135 0.107
OG2 LPL 0.052 0.040 0.066 0.048 0.151 0.117
OG3 HPL 0.048 0.030 0.059 0.042 0.151 0.098
OG3 MPL 0.046 0.035 0.058 0.043 0.145 0.099
OG3 LPL 0.050 0.036 0.061 0.046 0.125 0.119
P3D HPL 0.054 0.057 0.066 0.063 0.160 0.134
P3D MPL 0.063 0.064 0.077 0.074 0.179 0.235
P3D LPL 0.082 0.085 0.107 0.112 0.233 0.376
Fig. 7 The result of the feature importance test (A) and the breakdown analysis (minimum value (B), median value (C), maximum value (D)) of
the horizontal accuracy
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 10 of 19
Vertical Accuracy—Road Surface
When comparing the two study areas, variations in error
ranges were observed, depending on the data collection
method and processing level. Specifically, the Úrrétje area
showed lower error values in 12 instances, while the oblique
and P3D methods yielded superior results for the Csapók-
ert area. Regarding RMSE, the Úrrétje area demonstrated
better outcomes in 18 cases, with only three exceptions.
These exceptions occurred in OG1 LPL, where the differ-
ence between the two areas was 3.5cm, and in OG2 for OG3
LPL, with a difference of less than 0.4cm. Compared to the
error ranges of different data collection methods at the same
level, the NG1 method consistently demonstrated the highest
accuracy. However, in both cases, the error range exceeded
1m. This was notably observed with the NG3 LPL method
(1.04m) for the Csapókert area and the P3D LPL method
(1.15m) for the Úrrétje area (Fig.8). In both areas, the uti-
lization of the double-grid approach for the oblique method
showed a significant improvement in the range values com-
pared to the single-grid approach.
Regarding RMSE, our results did not identify a univer-
sally superior method. Instead, they indicated that the OG3
method provided the lowest values of HPL in the Csapókert
area, while the NG1 method was the best for MPL and LPL.
In the Úrrétje area, NG3 achieved the best results for HPL
and MPL, whereas NG1 obtained the best results for LPL
(Table3).
When comparing different processing levels, it was
observed that in the Csapókert area, MPL yielded lower val-
ues than HPL for the NG1 and NG2 methods. In the Úrrétje
area, only the NG2 method demonstrated lower values in
the MPL than in the HPL. In terms of RMSE, only the NG1
and NG2 methods in the Csapókert area failed to exhibit
lower errors for the HPL than for the MPL. Notably, we
achieved 2.4 times lower errors in the error range (MPL-
HPL) using the P3D method in the Csapókert area and 2.9
times lower errors using the OG3 method in the Úrrétje area.
However, similar improvements were not observed for the
RMSE values.
According to the feature importance test results, the pro-
cessing level exerted the most substantial influence on the
Fig. 8 The result of the vertical accuracy assessment on the road surface
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 11 of 19 15
alteration of vertical accuracy values for roadways, register-
ing a value of 0.1307. In contrast, the study area had the least
impact, with a value of 0.1148. There was a more noticeable
disparity in the significance of the different factors compared
to the previous reference type. Further examination through
the breakdown analysis unveiled intriguing insights.
When examining the minimum value (negative error) for
the Csapókert area, the P3D mode, LPL, and E-W direc-
tional flight increased the magnitude of the error (Fig.9). In
contrast, for the median error in the Úrrétje area, the tilted
camera, HPL, and double grid slightly increased the differ-
ence from zero. At the maximum value, except for the study
area (Csapókert), the other three factors—oblique camera
angle, MPL, and E-W directional flight—increased the error.
Vertical Accuracy—Roof
Assessment of the accuracy of the roof surfaces revealed
substantial errors and range values. Upon comparison of
the two study areas, it was evident that the Úrrétje area
consistently demonstrated superior performance over the
Csapókert area across all processing methods and levels,
with disparities frequently exceeding several tens of cen-
timeters. However, regarding RMSE values, the Csapókert
area exhibited lower errors in six instances, specifically for
the NG1 HPL, MPL, LPL, NG2 HPL, MPL, and Perim-
eter 3D LPL methods. The differences in RMSE between
the two areas are notably smaller than the range of error
values. When comparing the various data collection meth-
ods, it was found that the P3D method consistently yielded
the smallest range of errors at the same processing lev-
els in both study areas. Additionally, when focusing on
the HPL and MPL, the OG3 method demonstrated con-
siderable improvement in results compared to the other
methods, with differences of only a few centimeters lower
than those of P3D. Regarding the RMSEs in the Csapókert
area, the OG1 method exhibited the lowest values for HPL
(21.6cm) and MPL (22.7cm). The NG1 method produced
the lowest values for LPL (23.8cm). In the Úrrétje area,
the NG3 method consistently performed the best across all
three processing levels, with RMSEs of 15.3cm for HPL,
15.6cm for MPL, and 16.4cm for LPL. Conversely, the
P3D method yielded the poorest RMSE results for both
the areas.
Concerning various processing levels, the considered
reference type exhibited a noteworthy number of instances
where the anticipated hierarchy (HPL > MPL > LPL) was not
consistently observed. Regarding the range of errors, in the
Csapókert area, MPL produced superior results for the NG1
and NG2 methods, while the LPL level yielded the best out-
comes for OG1 (refer to Fig.10). Conversely, in the Úrrétje
area, the expected relationship was only observed for OG1.
Upon analyzing the models generated from the three nadir
flights, it was noted that the LPL resulted in the narrowest
Table 3 Values in meters of
the median, range, and RMSE
of the vertical precision test of
the road
Acquisition Processing level Median Range RMSE
Csapókert Úrrétje Csapókert Úrrétje Csapókert Úrrétje
NG1 HPL − 0.010 − 0.003 0.363 0.147 0.068 0.034
NG1 MPL − 0.006 0.006 0.313 0.192 0.062 0.038
NG1 LPL − 0.027 − 0.001 0.620 0.426 0.091 0.064
NG2 HPL 0.005 0.022 0.850 0.295 0.110 0.048
NG2 MPL 0.014 0.035 0.452 0.270 0.087 0.057
NG2 LPL − 0.002 0.022 0.934 0.848 0.122 0.098
NG3 HPL 0.008 0.002 0.501 0.177 0.100 0.028
NG3 MPL 0.017 0.010 0.522 0.205 0.107 0.038
NG3 LPL 0.009 0.007 1.044 0.464 0.140 0.080
OG1 HPL − 0.026 0.049 0.557 0.654 0.101 0.079
OG1 MPL − 0.017 0.042 0.754 0.839 0.119 0.097
OG1 LPL − 0.038 0.009 0.758 0.972 0.136 0.172
OG2 HPL 0.003 0.018 0.434 0.263 0.084 0.052
OG2 MPL − 0.006 0.033 0.486 0.629 0.090 0.082
OG2 LPL − 0.053 − 0.009 0.829 0.892 0.141 0.145
OG3 HPL 0.009 0.020 0.370 0.189 0.057 0.040
OG3 MPL 0.001 0.044 0.405 0.550 0.068 0.074
OG3 LPL − 0.035 − 0.005 0.652 0.726 0.125 0.128
P3D HPL − 0.033 − 0.029 0.391 0.473 0.073 0.069
P3D MPL − 0.083 − 0.042 0.925 0.713 0.238 0.121
P3D LPL − 0.234 − 0.226 0.999 1.157 0.408 0.370
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 12 of 19
range of errors, while the MPL proved most effective for the
OG2, OG3, and P3D methods.
Examining the RMSEs in the Csapókert area, it was
found that the HPL yielded the lowest error values in three
cases (NG2, OG1, and OG2), with no discernible dif-
ference between the HPL and MPLs in one case (NG3)
(Table4). MPL resulted in the lowest errors for NG1 and
OG3, whereas LPL produced the lowest errors for the P3D
method. In the Úrrétje area, the best results were obtained
with the HPL for NG2, NG3, OG1, and OG2 methods, while
the LPL yielded the lowest RMSE values in the remaining
three cases.
The analysis of feature importance revealed valuable
insights regarding the vertical accuracy test conducted on
the rooftops (Fig.11). Image acquisition exerted the most
substantial influence on accuracy, with a value of 0.2247. In
contrast, the processing level had the smallest impact, with
a value of 0.1543, which was notably lower than that of the
other three factors.
Further investigation through breakdown analysis indi-
cated intriguing findings. In the case of the minimum value,
it was found that the Csapókert area, P3D mode, LPL, and
P3D flight direction increased from zero. Considering the
median value, the Csapókert area and LPL decreased the
error; however, the tilted camera had no discernible effect,
while the E-W flight direction resulted in an increase. For the
maximum, it was observed that the Úrrétje area, nadir cam-
era angle, MPL, and E-W flight direction increased the error.
Data Collection andProcessing Time
Considerable disparities in flight duration were evident in
the analysis of the data collected. Single-grid flights dem-
onstrated the shortest flight durations, as they followed the
same trajectory irrespective of the camera angles, yielding
negligible differences. In contrast, P3D flights were char-
acterized by the longest duration. The duration of double-
grid flights was less than twice that of single-grid flights,
as detailed in Table5. By comparing the two surveyed
areas, it was observed that the average flight time in the
Úrrétje area was 17% longer. Notably, within this area,
the flight time associated with P3D exceeded the recom-
mended overall flight time of 21min by the drone manu-
facturer (Internet 1).
Upon analysis of processing times, our findings revealed
an approximately threefold increase in computation time
across distinct levels, regardless of the data collection
methods employed. In the comparison between the two
surveyed regions, the processing time for the Úrrétje data
was observed to be 34% longer on average (Fig.12). Spe-
cifically, at identical processing levels, NG3 processing
required the greatest amount of time, whereas OG1 pro-
cessing required the least time. Remarkably, data acquired
through the double-grid method took approximately three
times longer to process compared to each single grid for
all three processing levels in both areas.
Fig. 9 The result of the feature importance test (A) and the breakdown analysis (minimum value (B), median value (C), maximum value (D)) of
the vertical accuracy of the road
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 13 of 19 15
Discussion
Experimental investigations were conducted in two distinct
urban study areas with different built-up characteristics. This
approach facilitated the comparative analysis and validation
of general statements regarding the accuracy of UAS-based
mapping outputs in urban environments. Overall, our study
observed better accuracies and modeling reliability in the
case of the Úrrétje area compared with Csapókert. This dis-
crepancy can be attributed to the spatial distribution of the
GCPs. In the Csapókert area, the GCP distribution followed
the surface of four parallel roads, resulting in a relatively
regular pattern that was ultimately deemed suboptimal.
Consistent with prior studies by Hugenholtz etal. (2016)
and Ahmed etal. (2022), our investigation confirmed that
the horizontal accuracy rates exceeded the vertical accuracy
rates.
In the context of the horizontal accuracy test results, it
was evident that the accuracy values improved with higher
processing levels, consistent with our anticipated outcomes.
The chosen data acquisition methods played a pivotal role
in the surveys, with NG1 yielding the most favorable results
across all three tested factors (RMSE, error range, and
median), whereas P3D demonstrated comparatively inferior
outcomes (Table2).
These findings notably differ from those reported in stud-
ies, where the tilted camera and the double-grid method were
reported to achieve the highest accuracy. In our study, the
double-grid method only demonstrated accuracy improve-
ment for the tilted camera angle, in contrast to the results
of Ahmed etal. (2022) and Strząbała etal. (2022), where
the double-grid method enhanced the accuracy of nadir
acquisition.
The disparities in the results could be attributed to vari-
ations in data collection methods. Specifically, our study
employed a flight altitude that was 30m higher, and the
camera tilt angle was 10° more pronounced during oblique
shooting than in the study by Ahmed etal. (2022). Simi-
larly, Jaud etal. (2018) found that a tilted camera provided
better horizontal accuracy in Agisoft PhotoScan (the older
version of Agisoft Metashape software). The variation in
outcomes between our study and theirs could also originate
Fig. 10 The result of the vertical accuracy assessment on the roof tops
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 14 of 19
Table 4 Values in meters of
the median, range, and RMSE
of the vertical precision test of
the roof
Acquisition Processing level Median Range RMSE
Csapókert Úrrétje Csapókert Úrrétje Csapókert Úrrétje
NG1 HPL − 0.164 0.264 1.058 0.795 0.255 0.338
NG1 MPL − 0.148 0.263 0.899 0.775 0.235 0.349
NG1 LPL − 0.135 0.270 1.020 0.731 0.238 0.337
NG2 HPL 0.199 0.252 1.155 0.890 0.306 0.321
NG2 MPL 0.225 0.268 1.004 0.927 0.312 0.343
NG2 LPL 0.252 0.230 1.291 0.805 0.353 0.325
NG3 HPL 0.219 − 0.009 1.090 0.738 0.309 0.153
NG3 MPL 0.239 − 0.002 1.133 0.754 0.309 0.156
NG3 LPL 0.282 0.063 1.101 0.728 0.346 0.164
OG1 HPL 0.028 − 0.100 1.318 0.769 0.216 0.187
OG1 MPL 0.054 − 0.096 1.386 0.897 0.227 0.201
OG1 LPL 0.124 − 0.055 1.183 0.975 0.275 0.192
OG2 HPL 0.321 − 0.026 1.080 0.920 0.374 0.167
OG2 MPL 0.320 − 0.001 1.096 0.878 0.389 0.171
OG2 LPL 0.347 0.060 1.449 1.063 0.428 0.217
OG3 HPL − 0.258 − 0.237 0.781 0.711 0.284 0.280
OG3 MPL − 0.214 − 0.234 0.887 0.696 0.268 0.266
OG3 LPL − 0.204 − 0.170 1.047 0.904 0.276 0.239
P3D HPL − 0.538 − 0.519 0.767 0.703 0.544 0.527
P3D MPL − 0.537 − 0.501 0.817 0.655 0.550 0.513
P3D LPL − 0.423 − 0.443 0.872 0.858 0.447 0.464
Fig. 11 The result of the feature importance test (A) and the breakdown analysis (minimum value (B), median value (C), maximum value (D))
of the vertical accuracy of the roof
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 15 of 19 15
from differences in data acquisition methods. Their oblique
survey employed a 40-m lower flight altitude and a 20°
higher camera tilt angle compared to ours. A recent study
by Mueller etal. (2023) introduced innovative UAS flight
design scenarios, yet it is important to highlight that their
conceptual framework was specifically tailored for applica-
tion within urban environments.
The results of the vertical accuracy test revealed distinct
impacts of flight direction and camera angle factors on the
RMSE values across the two study areas and the three pro-
cessing levels. Notably, in the Csapókert area, the tilted dou-
ble grid (OG3) method produced superior results for ground
points at the HPL, consistent with the findings reported in
studies by Jaud etal. (2018) and Ahmed etal. (2022). In
contrast, in the Úrrétje area, the NG3 method achieved the
highest accuracy, mirroring the third-best vertical accuracy
result obtained in the study by Ahmed etal. (2022).
For vertical accuracy on road surfaces in the Úrrétje area,
our results corroborated a similar trend observed by Dai
etal. (2023) study, indicating that a tilted camera does not
necessarily lead to increased accuracy when a substantial
number (> 5–8) of GCPs are employed. The optimal method
for rooftop points was OG1 in the Csapókert area and NG3
in the Úrrétje area. In conclusion, in line with the findings
of Nesbit and Hugenholtz (2019), our results emphasize
that utilizing both a double-grid flight direction and tilted
Table 5 The flight times of the different acquisition methods
Csapókert Úrrétje
Single grid 0:08:51 0:10:17
Double grid 0:16:08 0:19:25
Perimeter 0:19:41 0:22:33
Fig. 12 The processing times of the different processing levels and data acquisition methods within the two study areas
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Journal of Geovisualization and Spatial Analysis (2024) 8:1515 Page 16 of 19
camera angle contributes to improved vertical accuracy
(Tables3 and 4) compared to employing a single grid and
nadir configurations for these two reference point types. Pre-
vious studies have shown that insituations involving oblique
camera angles, optimal accuracy levels are attained when
the automatic flight plan is supplemented by manual flight
operations for additional image acquisition (Kovanič etal.
2023).
Jakovljevic etal. (2019) observed that point clouds gen-
erated through UAS-SfM exhibited larger vertical errors,
resulting in an overestimation of elevations compared to
point clouds derived from LiDAR surveys. According to the
authors, these errors limited the suitability of SfM-generated
point clouds for applications such as accurately modeling
flood hazard areas. However, in our study, the vertical errors
of the ground surface points were, on average, 17cm smaller
than those in Kameyama and Sugiura (2020). Kameyama
and Sugiura (2020) examined different data acquisition
parameters, including flight altitude and image overlap
to assess the height and volume of forest trees. They dis-
covered that the methods of data collection had minimal
impacts on error magnitudes, which, in their study, proved
to be excessively large for the intended measurements. In
terms of non-ground surface vertical accuracy, our models
produced RMSEs within the sub-meter range, contrasting
with the RMSEs exceeding a meter defined by Kameyama
and Sugiura (2020).
It is essential to note that our study primarily focused
on the quantitative analysis of the unprocessed results gen-
erated by the SfM process. In contrast, prior research has
emphasized the potential for substantial enhancements in the
vertical accuracy of UAS-derived DEMs through the incor-
poration of weighted averaging and additive median filtering
algorithms (Ajibola etal. 2019). In our investigation of accu-
racy factors, we observed that the recording method held
the greatest significance for two reference types (horizontal
and vertical roofs), whereas the processing level emerged
as the most influential factor for the vertical roof reference.
Unlike Mora-Felix etal. (2020), we found that flight direc-
tion played a minor role in our study. The discrepancies in
the results between the two studies may be attributed to vari-
ations in the specific factors investigated.
The software processing time for the images produced
by various methods was aligned with the specifications pro-
vided by the software developer (Internet 2). According to
these specifications, projects with larger image datasets and
higher processing levels generally require longer process-
ing times. However, it is noteworthy that the accuracy of
the models produced from these processes does not consist-
ently surpass that of models produced with lower processing
levels. In contexts where the timely delivery of results is of
significant importance, as in applications such as agriculture
and monitoring, the optimization of SfM processing time
becomes paramount. Based on our findings, we infer that
employing MPL can offer a satisfactory level of accuracy for
situations where expeditious results are of primary concern.
Conclusions
The primary objective of this study was to evaluate and com-
pare the impact of various UAS flight orientations and cam-
era angles on the accuracy of SfM models, while considering
different processing levels. This investigation was conducted
across two urban study areas, with distinct characteristics
enabling a thorough examination of their combined effects.
In the present study, we generated 42 distinct models
including both digital surface models (DSMs) and ortho-
photo mosaics, by varying image acquisition and processing
parameters. Our evaluation of horizontal accuracy, specifi-
cally regarding ground elevation, through orthophoto mosa-
ics, consistently showed that the nadir camera angle com-
bined with the single-grid flight mode provided the highest
accuracy across all processing levels. Notably, we found that
image acquisition emerged as the primary factor influenc-
ing accuracy values, with minimal disparities among the
outcomes of the four tested factors.
On the other hand, our vertical accuracy assessment on
road surfaces did not identify a single optimal method for
all the processing levels. We observed instances where the
double-grid and oblique camera angles produced superior
results. The processing level had a more profound impact on
accuracy. Similarly, our examination of roof surface meas-
urements revealed that different data acquisition methods
across varied processing levels yielded optimal results, with
image acquisition exerting the greatest impact on accuracy.
When analyzing the processing levels for both vertical
references, we noted that, in several cases, the medium pro-
cessing level (MPL) outperformed the high processing level
(HPL) in terms of accuracy. This suggests that MPL could
serve as a viable alternative, particularly when rapid results
are required. Despite the potential benefits of oblique camera
angles and double grids in certain scenarios, it is essential to
consider the significant increase in processing time, associ-
ated with these methods, which may be a crucial factor in
practical applications.
Our research findings offer a comprehensive understand-
ing of the potential accuracy variations associated with dif-
ferent flight patterns and image acquisition modes. Collec-
tively, these results contribute substantially to the broader
applicability of UAS-based aerial mapping in urban settings.
According to our experiences, urban mapping is con-
strained by several factors. One of the most significant chal-
lenges is the regulatory framework that may impede lawful
surveys. Additionally, UAV flight time restrictions can pose
obstacles when mapping large areas. To further investigate
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Journal of Geovisualization and Spatial Analysis (2024) 8:15 Page 17 of 19 15
this subject, our team intends to explore the feasibility of
employing various external orientation techniques in urban
settings and their effect on model precision.
Funding Open access funding provided by University of Debrecen.
The research was funded by the K138079 project of the NKFI.
Data Availability The participants of this study did not give written
consent for their data to be shared publicly, so due to the sensitive
nature of the research supporting data is not available.
Declarations
Ethical Approval All ethical responsibilities for authors have been
read and adhered to. All authors approve manuscript with no ethical
misconduct.
Informed Consent The research does not involve any human partici-
pant or animal. It is strictly GIS and geospatial analysis. There is there-
fore no consent with respect to participants in any analysis.
Conflict of Interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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