Accuracy of Unmanned Aerial Systems Photogrammetry and Structure from Motion in Surveying and Mapping: A Review

Deliry, Sayed İshaq; Avdan, Uğur

doi:10.1007/s12524-021-01366-x

Cited by 49 publications

(50 citation statements)

References 120 publications

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“…Information gathered in digital format with the sensors carried by UAS, processed in photogrammetry software, become, most of the time, an alternative to existing topographic and planimetric maps. In addition, digital maps are often used in GIS and CAD applications for design analysis [14]; in this case, we refer to monitoring or data collection activities [17].…”

Section: Geographic Information Systems (Gis)mentioning

confidence: 99%

“…Unmanned Aircraft Systems (UAS), Unmanned Aerial Vehicles (UAV), Drones, or Remotely Piloted Aircraft Systems (RPAS) refer to the unmanned systems navigating in the air [14], capable of flying over hard-to-reach areas [15]. This study was based only on the references regarding civilian UAS, not those used in the military sector.…”

Section: Introductionmentioning

confidence: 99%

“…Special attention is paid to Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude [27], because the urban environment is the testing environment of most of the technological progress elements, in the context of complexity and diversity of needs that need to be solved. Use of UAS images and the structure in motion photogrammetry with stereo multi-view (SfM-MVS) enables rapid reconstructions of the surface geometry (digital elevation models, orthophotoplans) based on the achieved and overlapped images [14].…”

Section: Introductionmentioning

confidence: 99%

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The Role of UAS–GIS in Digital Era Governance. A Systematic Literature Review

et al. 2021

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UAS (Unmanned Aircraft Systems) technologies, also known as UAV (Unmanned Aerial Vehicle), drones, or Remotely Piloted Aircraft System (RPAS) and GIS (Geographic Information System) are recognised for the value of the results that can be achieved by their combined use. However, their use and the results achieved are rarely framed within the context of Digital Era Governance (DEG), an undertaking that would significantly reduce the capabilities of knowledge transfer from the academic and/or private environment to the public domain. The purpose of this study was to highlight, by a bibliometric analysis, the areas of proposed use of this team of tools and the extent to which these can enter the sphere of interest of public administrations, especially local ones. From a methodological point of view, based on the 439 articles filtered from the Web of Science database where UAS/UAV and GIS technologies were used, several bibliometric analyses have emerged. VOSviewer and R (Bibliometrix tool) were used to conduct the bibliometric analyses. Most scientific publications that used UAV technology as a working tool have predominant applicability in photogrammetry, while GIS applications are found in publications dedicated to image processing, landslides, and cultural and archaeological heritage. We point out that from the point of view of international cooperation, at the level of institutions or countries, certain international organisations from the USA, China, and the central and northern European states have a high interest in this topic, and a low cooperation between academia and public administration is exhibited. The conclusion is represented by the apparent lack of framing of the results of UAS–GIS technologies usage into wider and more topical contexts, such as digital era governance, and also a reduced applicability of the research results.

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Section: Geographic Information Systems (Gis)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Role of UAS–GIS in Digital Era Governance. A Systematic Literature Review

et al. 2021

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“…The highly detailed digital surface model (DSM) and its ground-filtered derivative, digital terrain model (DTM), can be produced using airborne remote sensing [22]. Unmanned aircraft systems (UAS), in particular, allow photogrammetric mapping at a centimetre-level spatial resolution [23]. UAS mapping is an automated process providing quick results and flexibility, particularly in environments where conventional field surveys are laborious, such as wetlands [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…The photogrammetric elevation model is produced using UAS data with a structure-frommotion (SfM) machine learning algorithm which combines the neighboring images and determines depth information for each pixel when the images have sufficient overlap [26]. Centimetre-level accuracy can be achieved if a high-precision Global Navigation Satellite System (GNSS) unit, such as Real-Time Kinematic (RTK), is used to follow the aircraft location or if ground control points (GCPs) with known co-ordinates are used for georeferencing [23]. Photogrammetric SfM can reach a spatial accuracy comparable with laser-based light detection and ranging (LiDAR), except in the case of densely vegetated areas where the land surface remains mostly obscured for the cameras but not for all laser beams [18].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

et al. 2022

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Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

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