Multi-parametric Performance Evaluation of Drone-based Surveying for

Disaster Risk Management

Athina Silvestrou, Andreas M. Georgiou

, Panayiotis Kolios and Christos G. Panayiotou

University of Cyprus, KIOS Research and Innovation Center of Excellence,

1 Panepistimiou Avenue, P.O. Box 20537, 2109 Aglantzia, Cyprus

Keywords:

Aerial Photogrammetry, UAV, Flight Properties, Orthophoto, Digital Elevation Model, Metashape,

OpenDroneMap.

Abstract:

Disaster situations, either natural or man-made, could be catastrophic as causes massive destruction of infras-

tructures or loss of human lives. First-responders should conduct quickly and efﬁciently to locate survivors; an

operation that becomes difﬁcult when the existing infrastructure and the conventional communication might

collapse. Unmanned Aerial Vehicles have emerged as a reliable and cost-effective solution, which aids humans

in performing such operations by implementing accurate geographical surveying. The proper combination of

hardware and software components (for the design and implementation) has to be selected to produce appropri-

ate results, both with respect to output quality and processing time. In this paper, photogrammetric approaches

have been investigated in the terms of collection, processing and producing of dense clouds, orthophotos and

digital elevation models considering the aforementioned aspects in disaster risk management missions. Thus,

different datasets have been collected where the drone-based ﬂight along with sensing parameters were jointly

investigated, evaluating the total processing time and model quality. More speciﬁc, the following scenarios

were investigated: (i) Selection and combination of the photogrammetric method parameters, (ii) Sensor alti-

tude, and (iii) Area division into separated sectors. The decision about the processing software has be done

after assessing the capabilities and the limitations of each solution, since this will affect time, quality and cost

of the end result. Under this perspective, a commercial and non-commercial tools compared for the data anal-

ysis. Finally, the results of each tool were evaluated while the capabilities and limitations have been perceived.

1 INTRODUCTION

Disaster situations are catastrophic events that are im-

portant problems in all the areas of the world, both

developed and developing. The problem arises from

the extent of the disaster, which either can be natu-

ral (geophysical, hydrological, climatological or me-

teorological) or man-made, which causes massive de-

struction of infrastructures and loss of human lives.

The ﬁrst few hours following a disaster may be con-

sidered as the golden relief time to save the lives of

several victims by providing emergency aid (Panda

et al., 2019). During the disaster, the existing infras-

tructure and the conventional communication might

collapse, meaning that the affected areas are dis-

connected without the ability to exchange informa-

tion. Therefore, it becomes difﬁcult from the ﬁrst-

responders to locate the survivors during the Search

And Rescue (SAR) operation that have to conduct

https://orcid.org/0000-0002-0989-4309

quickly and efﬁciently, and for the ﬁeld teams to col-

lect information, ranging from immediate metrics on

the event itself, to more speciﬁc damage, need and

recovery information (Kerle et al., 2014).

Nowadays, Unmanned Aerial Vehicles (UAVs)

have emerged as a reliable and cost-effective solu-

tion, which aids humans in performing SAR opera-

tions, gathering early and continuous intelligence of

a disaster site and monitoring variety of tasks such

as planetary exploration, wireless related services for

ground wireless devices, and many more (Al-Kaff

et al., 2018; Akram et al., 2020; Kerle et al., 2014).

Drone-based surveying, introduces a multiplicity

of additional factors that affect both accuracy and

completion times (Kr

ak et al., 2016). With respect to

ﬂight planning, higher travelling speeds and higher al-

titudes reduces data collection times at the expense of

lower sensing resolution (Yusoff et al., 2017). In ad-

dition, most positioning devices onboard drone plat-

forms, are generally not able to provide location in-

Silvestrou, A., Georgiou, A., Kolios, P. and Panayiotou, C.

Multi-parametric Performance Evaluation of Drone-based Surveying for Disaster Risk Management.

DOI: 10.5220/0011038300003185

In Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2022), pages 123-129

ISBN: 978-989-758-571-5; ISSN: 2184-500X

123

formation with accuracy more than ±5m. Real Time

Kinematic (RTK) and Direct Real Time Kinematic

(DRTK) technology could be deployed at the expense,

however, of shorter ﬂy times and travel distances (due

mainly to the increasing weight and limited available

transmit power). Nevertheless, drone-based survey-

ing stems as a highly favorable approach for on-time

surveying especially in disaster risk management op-

erations as exempliﬁed above. Many methods and

sensors have been considered to date (P

ołka et al.,

2018) and drone-based approaches have unlocked ca-

pabilities that could not be possible before; especially

in hard to reach areas (Giordan et al., 2017; Ruzgien

et al., 2015).

UAV platforms, can offer sensing measurements

at the low capital and operating cost. Among their

beneﬁts are: a) high portability, b) high ground reso-

lution (Nex and Remondino, 2014), c) their ability to

deploy easily, d) the ease of capturing images/ videos,

e) the ability of drones to ﬂy at arbitrary altitudes

ołka et al., 2018; Giordan et al., 2017) enables col-

lection of data (images/ videos) of variable accuracy

depending on the mission. Importantly, drone plat-

forms are easily programmed to address varying re-

quirements, and thus, easily adapt to the user needs

(Yeong et al., 2015). Moreover, the high spatial and

temporal resolution of onboard sensors and the ability

to carry multiple such sensors like 3D sensors or near

infrared sensors (Doulamis et al., 2017), enable sig-

niﬁcant uptake of this technology (Rao et al., 2016).

According the EU funded project INACHUS con-

clusions (INACHUS Project, 2019), ﬁrst responders

given the strong time pressure on their work, require

some ﬂexible solutions that is able to deliver timely

data and with frequent update possibility. In addi-

tion, they need data that are easy to produce and quick

to interpret also by non-remote sensing experts. All

these, regard to low-cost and easily deployable and

replicable solutions (Nex et al., 2019).

The aim of this work, is to investigate how ﬂight

planning parameters and image processing procedure

can affect the total ﬂight mission time of an a UAV

participation in disaster management by providing sit-

uational awareness in less time. More speciﬁc, the

following points were investigated:

1. Software tools performance using selected pho-

togrammetric parameters.

2. How the model quality and processing time can

be affected by ﬂights on different altitudes.

3. How the division of the study area into sectors can

affect the processing time.

The rest of the paper is organized as follows; Section

2 details the proposed methodology approach while

Section 3 discuss the results obtained. Finally, con-

cluding remarks us drawn and future research direc-

tion are highlighted in Section 4.

2 PROPOSED METHODOLOGY

A survey project, has to lead to the decision of how

the 2 main steps such as ﬂight planning and image

processing, will be conducted. The needs and con-

strains of each survey mission rely on the considered

parameter values from which any inaccuracy could re-

sult massive volume of collected data, increased pro-

cessing time and geographical model estimation er-

rors. A survey, can be considered holistically by pro-

cessing the collected dataset in its unison to ensure

quality. Moreover, when time is of essence then the

various steps of the photogrammetry procedure can

be designed and executed in a parallel fashion (Vacca

et al., 2017; Xing et al., 2010). Under this perspec-

tive, the present methodological framework involves

a coherence of several stages, aiming to investigate

the latter fact and jointly consider ﬂight planning pa-

rameters, along with the image processing procedure,

in order to expedite the creation of the geographic

model. More speciﬁc, this work investigates how the

following cases/parameters can affect the total mis-

sion time and model quality:

1. Calculation of different photogrammetric method

parameters using different software packages.

2. Flight altitude.

3. Area sectorization.

Figure 1, provides a schematic diagram of the pro-

posed methodology architecture with the performed

operational functions. Level 1, refers to ﬂight plan-

ning where several parameters has to be taken into

consideration such as selection of the appropriate sen-

sor and platform. In addition, some parameters have

to be deﬁned regarding image overlapping percent-

age, ﬂight altitude, area boundaries, etc. Level 2,

refers to the image processing parameters that in-

vestigated and the photogrammetric products that de-

rived, which includes: (i) Camera alignment result-

ing the tie points, (ii) Dense point cloud generation

resulting the depth maps and dense cloud, (iii) 3D

Model resulted through mesh and digital elevation

model (DEM) products, and (iv) Orthophoto produc-

tion to eliminate the effect of image perspective and

correction of relief shifts caused by terrain condi-

tions. In this level, the performance and sensitiv-

ity of both commercial and non-commercial solutions

were also investigated. Precisely, the commercial

Agisoft Metashape software and the non-commercial

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

124

photogrammetric package of OpenDroneMap (ODM)

were used, that have the ability to establish relative

camera positions and use these positions to create ac-

curate three-dimensional models of the ground sur-

face. The models are textured by draping the aerial

photographs over them to produce photo-realistic

three-dimensional rendered outputs of the ground sur-

face (Quartermaine et al., 2013). Finally, on Level 3,

the results were extracted and grouped according the

investigated cases.

Figure 1: Proposed methodology architecture.

2.1 Implementation Aspects

In order to investigate all cases, 12 ﬂights were con-

ducted using the same UAV and camera parameters

for all the ﬂights. DJI Mavic 2 Enterprise was em-

ployed with an onboard sensor of 1/2.3” CMOS (com-

plementary metal oxide semiconductor) and effective

pixels 12M along with a lens of 82.6° ﬁeld of view at

35mm and an aperture of f/2.8. Both the forward (p%)

and the lateral (q%) overlap were ﬂuctuated between

75-80%. The image location is planned to guarantee

the results quality with almost the minimum overlap

in each point of the surveyed area. Both position and

acquisition order are optimized to minimize the num-

ber of acquired images and therefore, their acquisition

time. The ﬂight strip orientation is set parallel to the

longer side of the surveyed area, as the image collec-

tion along the same strip is faster than across adja-

cent strips, minimizing the same time the number of

of strips (Nex et al., 2019). The survey area was 220

m long and 170 m wide covering an area of 0.04 km

(Figure 2). Flight planning was completed using the

SwiftTAG software that developed from KIOS CoE of

the University of Cyprus and the UAV ﬂight was au-

tonomous allowing the automated generation of way-

points on the surveyed area. SwiftTAG software has

been chosen, as an open-source solution which has the

ability to be customized according the ﬂight needs.

The PC used for the analysis, has 6GB of RAM, an i5-

7500 CPU core and an internal hard drive for 20TB.

A certain amount of points (ﬁeld courts, poles

Figure 2: Flight plan of the surveyed area. The dots indicate

the waypoints of the strips.

and building corners) clearly identiﬁed in the images

where selected and measured by the method of RTK

GPS survey and used as ground control points (GCPs)

for georeferencing the data. Consequently, 55 targets

were placed evenly throughout the entire area before

the ﬂight. The coordinates of the targets were deter-

mined using the Stonex S900 GNSS receiver. Ac-

cording to the manufacturer, the RTK performance of

this receiver is 8 mm + 1 ppm RMS × the distance

to next reference station horizontally and 15 mm + 1

ppm RMS × the distance to next reference station ver-

tically. Figure 3, shows the location of the GCPs as

distributed on study area.

Figure 3: Spatial allocation of GCPs.

For each ﬂight, speciﬁc settings and characteris-

tics have been chosen comparing them in as simi-

lar conditions as possible, including weather condi-

tions (under sunny, cloudless and breezeless weather

conditions) and same ﬂight speed (3 m/s). Flights

Multi-parametric Performance Evaluation of Drone-based Surveying for Disaster Risk Management

125

A-B refer to different area with the same altitude,

where the datasets have been processed using both

Agisoft Metashape from Agisoft LLC and ODM as

photogrammetric packages. The total processing time

and the sensitivity of each package was the primary

performance indicator. Flights C-E were designed

to investigate the impact of altitude on the process-

ing time. The collected datasets from the respective

ﬂights captured images at 60 m, 90 m and 120 m

height in the same area. Finally, ﬂights F-L have

been conducted to investigate the total processing

time when a particular area of interest (AOI) is split

into sectors. Speciﬁcally, ﬂight F captured the full

AOI, while ﬂights G and H split the AOI into two

equal sectors and ﬂights I-L split the AOI into 4 equal

sectors.

Table 1, summarizes all information about the col-

lected 1.467 images (no oblique images were col-

lected) along with their associated parameters.

Table 1: Collected data information.

Parameter

Flight Image No p% q% Alt/ (m) Resolution

Value

A 54 75 75 80 4Kx2K

B 49 80 80 120 4Kx2K

C 143 85 85 60 4Kx2K

D 73 85 85 90 4Kx2K

E 49 85 85 120 4Kx2K

F 326 85 85 40 4Kx2K

G 186 85 85 40 4Kx2K

H 182 85 85 40 4Kx2K

I 122 85 85 40 4Kx2K

J 94 85 85 40 4Kx2K

K 109 85 85 40 4Kx2K

L 110 85 85 40 4Kx2K

3 EXPERIMENTAL RESULTS

3.1 Photogrammetric Parameters

Average time processing values have been ex-

tracted performing several repetitions on the col-

lected dataset, using the same processing unit. Ta-

ble 2, summarizes the results on each photogrammet-

ric stage calculation, using both commercial and non-

commercial software tools for 2 distinct datasets. As

indicated in the table, ODM did not manage to com-

plete all stages of the photogrammetric product in or-

der to produce the orthophoto and this is the main rea-

son for the signiﬁcantly reduced execution time.

It has to be noted, that Agisoft Metashape offers

6 accuracy levels, from which the 3rd (medium ac-

curacy) has been selected to account for the results

produced by ODM. The batch processing option on

the Agisoft Metashape environment, provided an au-

tomated processing operation, minimizing the user in-

teraction.

Table 2: Processing time Vs Software package.

Time (s)

Task Metashape ODM

Dataset A

Align 94,25 X

Dense Cloud 429,75 X

Mesh 3.333,00 Not Produced

Texture 145,75 Not Produced

Tiled Model 690,25 X

DEM 7,00 Not Produced

Orthophoto 150,75 X

Total Time 4.850,75 1.020,00

Dataset B

Align 102,33 X

Dense Cloud 2.160,67 X

Mesh 4.591,00 Not Produced

Texture 173,00 Not Produced

Tiled Model 356,67 X

DEM 3,00 Not Produced

Orthophoto 87,00 X

Total Time 7.467,45 1.620,00

Figure 4, presents the resulted 4 orthophotos using

both software packages for the 2-dataset collection.

Figure 4: Orthophotos as created for both dataset packages

[a] and [b].

The details of RMS errors for the GCPs and the

resulted orthophotos for the 2-dataset collection, us-

ing both Agisodt Metashape and ODM software pack-

ages, are in Table 3.

3.2 Altitude Impact

In order to investigate the impact of altitude on the

processing time, 3 ﬂights were performed at 60 m,

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

126

Table 3: Measured errors between generated orthophotos

and GCPs.

Residual

GCP Agisoft [a] ODM [a] Agisoft [b] ODM [b]

29 0,1043 0,1899 0,7338 0,1242

30 0,1599 0,1913 1,5234 0,3148

31 0,1215 0,1350 0,7276 0,2373

36 0,2722 0,0468 0,6699 0,0628

47 0,0518 0,1615 0,4124 0,0509

49 0,0829 0,4352 1,7566 0,0661

RMSE (m) 0,1499 0,2268 1,0873 0,1740

90 m and 120 m respectively. The UAV imagery

have been processed using AgiSoft Metashape soft-

ware package.

Table 4, summarizes the results on time process-

ing for the production of each photogrammetric prod-

uct for height-missions C to E. As is shown, altitude

change, has a signiﬁcant impact on the processing

time, reducing from 17.325 s on a 60 m altitude to-

wards to 7.521 s on a 120 m altitude.

Table 4: Processing time Vs Flying altitudes.

Time (s)

Task 60 m 90 m 120 m

Align 434,8 180,6 105,2

Dense Cloud 6.796,4 2.753,8 2.172,8

Mesh 8.593,2 4.647,2 4.570,0

Texture 435,6 239,4 174,0

Tiled Model 845,8 462,1 366,2

DEM 6,2 3,6 3,2

Orthophoto 212,8 150,2 129,8

Total Images 143 73 49

Total Time 17.324,8 8.433,9 7.521,2

Figure 5, presents the processing time for each ex-

amined parameter per altitude level. It is obvious that

in high altitudes, where there are less images cover-

ing the AOI, the processing time reduces signiﬁcantly.

Dense and cloud mesh, stand out as the parameters

with the major reduction on processing time when in-

creasing the image capture altitude.

Figure 5: Processing time (sec) per altitude for each exam-

ined parameter.

Figure 6, presents a sample of the UAV imagery

processing in Agisoft Metashape software environ-

ment, at 60 m ﬂight altitude. The blue rectangles

represent the collected images as distributed over the

AOI, where the black axes refer to the platform posi-

tion on capturing time as it is calculated with a ±5m

GPS accuracy. In addition, a 3D model has been ex-

tracted of the AOI.

Figure 6: Image processing, where: (a) Distribution of im-

ages collected to cover the AOI, and (b) 3D model extrac-

tion of the AOI.

Figure 7, presents the resulted orthophotos per

examined altitude ﬂight and as ﬁtted on the respect

scene from Google satellite.

Figure 7: Resulted orthophotos for each altitude ﬂight com-

pared to the respect scene of Google satellite basemap.

Table 5, summarizes the total error values on

GCPs for the resulted orthophotos per altitude.

Table 5: Measured errors between generated orthophotos

per altitude and GCPs.

Residual

GCP 60 m 90 m 120 m

25 0,1584 0,3692 0,3690

26 0,0487 0,1606 0,4107

27 0,3064 0,1664 0,0839

30 0,1309 0,2409 0,1559

31 0,2011 0,1958 0,1453

36 0,1389 0,3060 0,3573

47 0,1988 0,2140 0,2961

49 0,1173 0,2759 0,1466

RMSE (m) 0,1773 0,2504 0,2725

3.3 AOI Coverage

The processing time of a selected AOI, proved to be

signiﬁcant depended with the respect covered area.

Table 6, summarizes the processing time when area

Multi-parametric Performance Evaluation of Drone-based Surveying for Disaster Risk Management

127

sectorization is applied in each overlap-mission. As

it is shown, when the dataset covered the whole AOI,

the total completion time was less than the total sum

time of the subdivided areas 2a and 2b respectively.

The same trend, is also observed on the next datasets

where the AOI is divided into 4 sectors. The sum

value of the total completion time of the last four

datasets (4a-4d), has been approximately the same

with the sum value of datasets 2a and 2b respectively.

In the same way, this sum value is also close enough

to total completion time of the dataset refereed to the

whole AOI (a). Importantly though, smaller sectors

enable parallelization in the execution and thus sig-

niﬁcantly slash processing time.

In Figure 8, the resulted orthophotos are illus-

trated, as derived from AOI sectorization, with the re-

spect ﬁtted scene from Google Satellite.

Figure 8: Resulted orthophotos for each ﬂight with different

AOI boundaries, compared to the respect scene of Google

satellite basemap.

4 CONCLUSIONS

The proper combination of both hardware and soft-

ware solutions is necessary to design and implement

an accurate geographical survey. These components,

are vital in the production of appropriate results in

the terms of output quality and processing time, espe-

cially for disaster risk management. This paper, pro-

vides a very brief overview on how the total mission

time and model quality can be affected during the pro-

cessing of speciﬁc photogrammetric parameters. The

investigated cases based on empirical ﬁndings from

an extensive surveying campaign totaling 12 ﬂights

using various parameter settings (including altitude

and coverage areas). In addition, a comparison of

state-of-the-art commercial and non-commercial pho-

togrammetric software was conducted to demonstrate

the possible beneﬁts in completion time from the use

of different data processing algorithms.

The major ﬁndings of this work, can be summa-

rized in some key points. First, the height of the

ﬂight plays signiﬁcant role on time measurements and

achieved accuracy. It is associated with the resulted

orthophoto and the value of height should be calcu-

lated along with the focal length of the camera to

meet the needed requirements. However, when there

is a margin, a higher altitude could be considered for

less ﬂight mission and less processing time. Another

key point is that the side and forward overlap of im-

ages is essential to acquire a higher accuracy which

increases the time has to be spend for acquiring im-

ages and processing them in the software. However,

smaller sectors enable parallel processing that signif-

icantly slash processing time. Finally, regarding the

analysis software package, AgiSoft Metashape com-

mercial software proved to be a reliable solution for

data processing.

4.1 Future Recommendation

Given the importance of model accuracy when de-

signing time-sensitive surveys, the following aspects

should be taken into consideration for future work.

Use and test additional photogrammetric software

in order to understand the limitations and capabilities

of various algorithms employed. More commercial

and non-commercial packages could be evaluated for

their performance and their end results quality, in or-

der to identify the best combination on processing

time and model accuracy. Moreover, a further re-

search is needed on how the quality of the orthophotos

can be regulated to further improve the overall pro-

cessing time. In addition, several parameters affect-

ing the ﬂight should be taken into consideration that

might affect the survey overall measurements time

and the output quality and accuracy. The drone size

(mainly for its’ vulnerability to weather conditions

such as wind) and drone speed while taking photos

should be investigated as well along with the inﬂu-

ence of weather conditions and solar positioning dur-

ing the UAV survey.

Another aspect that should take into considera-

tion, is the use of Artiﬁcial Intelligence (AI) tech-

niques to: (1) automate processes that require heavy

manual operation; (2) improve performance of pro-

cesses in terms of efﬁciency and robustness (Qin and

Gruen, 2021).

GISTAM 2022 - 8th International Conference on Geographical Information Systems Theory, Applications and Management

128

Table 6: Processing time Vs Sectorized AOI.

Time (s)

Task

AOI AOI/2 AOI/2 AOI/4 AOI/4 AOI/4 AOI/4

(a) (2a) (2b) (4a) (4b) (4c) (4d)

Align 972,5 411,5 463,7 222,5 196,7 223,5 231,2

Dense Cloud 11.274,7 6.046,2 6.427,7 3.084,2 2.413,2 3.469,0 4.698,5

Mesh 17.100,0 10.809,2 10.185,0 4.682,7 3.416,7 4.740,5 5.887,2

Texture 710,0 414,7 405,5 275,0 21,0 255,2 256,5

Tiled Model 1.293,5 789,0 762,7 590,5 487,7 531,2 448,5

DEM 13,7 7,2 6,2 6,0 4,7 4,2 4,2

Orthophoto 649,7 434,5 429,7 168,0 141,0 182,2 212,5

Total Time 30.014,25 18.912,5 18.680,7 9.029,7 6.875,2 9.406,0 11.738,7

,→ ←- ,→ ←-

37.593,2 37.049,6

ACKNOWLEDGEMENTS

The work of authors, has been partially supported by

the European Union’s Horizon 2020 research and in-

novation program under grant agreement No 739551

(KIOS CoE) and by the Republic of Cyprus through

the Deputy Ministry of Research, Innovation and Dig-

ital Policy.

REFERENCES

Akram, T., Awais, M., Naqvi, R., Ahmed, A., and Naeem,

M. (2020). Multicriteria uav base stations place-

ment for disaster management. IEEE Systems Journal,

14(3):3475–3482.

Al-Kaff, A., Mart

ın, D., Garc

ıa, F., de la Escalera, A., and

Mar

ıa Armingol, J. (2018). Survey of computer vision

algorithms and applications for unmanned aerial vehi-

cles. Expert Systems with Applications, 92:447–463.

Doulamis, N., Agraﬁotis, P., Athanasiou, G., and Amditis,

A. (2017). Human object detection using very low res-

olution thermal cameras for urban search and rescue.

In Proceedings of the 10th International Conference

on PErvasive Technologies Related to Assistive Envi-

ronments, PETRA ’17, page 311–318, New York, NY,

USA. Association for Computing Machinery.

Giordan, D., Manconi, A., Remondino, F., and Nex, F.

(2017). Use of unmanned aerial vehicles in monitor-

ing application and management of natural hazards.

INACHUS Project (2019). Available online: www.inachus.

eu (accessed on 17 february 2022).

Kerle, N., Fernandez Galarreta, J., and Gerke, M. (2014).

Urban structural damage assessment with oblique uav

imagery, object-based image analysis and semantic

reasoning. In Proc., 35th Asian conference on remote

sensing.

ak, B., Bli

st’an, P., Paulikov

a, A., Pu

arov

a, P., Ko-

vani

c, L., Palkov

a, J., and Zeliz

nakov

a, V. (2016). Use

of low-cost uav photogrammetry to analyze the accu-

racy of a digital elevation model in a case study. Mea-

surement, 91:276–287.

Nex, F., Duarte, D., Steenbeek, A., and Kerle, N. (2019).

Towards real-time building damage mapping with

low-cost uav solutions. Remote Sensing, 11(3).

Nex, F. and Remondino, F. (2014). Uav for 3d mapping

applications: a review. Applied geomatics, 6(1):1–15.

Panda, K. G., Das, S., Sen, D., and Arif, W. (2019). Design

and deployment of uav-aided post-disaster emergency

network. IEEE Access, 7:102985–102999.

ołka, M., Ptak, S., Kuziora, Ł., and Kuczy

nska, A. (2018).

The use of unmanned aerial vehicles by urban search

and rescue groups. Drones-Applications. London: In-

techOpen, pages 83–96.

Qin, R. and Gruen, A. (2021). The role of machine intelli-

gence in photogrammetric 3d modeling–an overview

and perspectives. International Journal of Digital

Earth, 14(1):15–31.

Quartermaine, J., Olson, B. R., and Howland, M. (2013).

Appendix b: Using photogrammetry and geographic

information systems (gis) to draft accurate plans of

qazion. Journal of Eastern Mediterranean Archaeol-

ogy and Heritage Studies, 1(2):169–174.

Rao, B., Gopi, A. G., and Maione, R. (2016). The societal

impact of commercial drones. Technology in society,

45:83–90.

Ruzgien

e, B., Berte

ska, T., Ge

cyte, S., Jakubauskien

e, E.,

and Aksamitauskas, V.

C. (2015). The surface mod-

elling based on uav photogrammetry and qualitative

estimation. Measurement, 73:619–627.

Vacca, G., Dess

ı, A., and Sacco, A. (2017). The

use of nadir and oblique uav images for building

knowledge. ISPRS International Journal of Geo-

Information, 6(12):393.

Xing, C., Wang, J., and Xu, Y. (2010). Overlap analysis of

the images from unmanned aerial vehicles. In 2010 In-

ternational Conference on Electrical and Control En-

gineering, pages 1459–1462. IEEE.

Yeong, S., King, L., and Dol, S. (2015). A review on marine

search and rescue operations using unmanned aerial

vehicles. International Journal of Marine and Envi-

ronmental Sciences, 9(2):396–399.

Yusoff, A. R., Ariff, M. M., Idris, K. M., Majid, Z., and

Chong, A. K. (2017). Camera calibration accuracy

at different uav ﬂying heights. The international

archives of photogrammetry, remote sensing and spa-

tial information sciences, 42:595.

Multi-parametric Performance Evaluation of Drone-based Surveying for Disaster Risk Management

129