Geomorphometry from unmanned aerial surveys

Florinsky, Igor V.; Курков, В. М.; Bliakharskii, D. P.

doi:10.1111/tgis.12296

Cited by 19 publications

(11 citation statements)

References 58 publications

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“…Default PhotoScan values are used for processing with a couple of the key settings listed in Table 2. PhotoScan is selected as the primary modeling program based on performance, ubiquity in the literature [51,52,53] and processing reports. Eventually, the density of the point clouds, the error in location of the GCPs and CPs, and comparisons of time and photos required demonstrates the merit of multi-scale models and methods.…”

Section: Methodsmentioning

confidence: 99%

Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection

Okeson

Barrett

Arce

et al. 2019

Sensors

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This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire dam at high-priority level. This results in approximately 75% reduced flight time and model processing load, while still maintaining high model accuracy where needed. Models display stepped improvement in visual clarity and SfM reconstruction integrity by priority level, with the higher priority regions more accurately modeling smaller and finer features. A resolution map of the final tiered model is included. While this study focuses on multi-scale view planning for optical sensors, the methods potentially extend to other remote sensors, such as aerial LiDAR.

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Section: Methodsmentioning

confidence: 99%

Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection

Okeson

Barrett

Arce

et al. 2019

Sensors

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show abstract

“…In other words, it might be possible to increase the generalization capacity of the model by enriching the training database through diversifying the samples (e.g., adding more scenes from different cities). To rapidly acquire high-accuracy topographic data in these small and typical areas, the UAV photogrammetry is now entirely competent [56][57][58]. In theory, the larger area the training dataset covers, the more features of the urban topography can be learned.…”

Section: Training Datamentioning

confidence: 99%

A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Jiang

Xia

et al. 2020

Water

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The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

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“…To consider the lens distortion, we previously performed a laboratory photogrammetric calibration of the camera, as well as a field camera calibration [1]. The following camera settings are used during an aerial survey: (1) The lens is locked to focus to infinity; (2) The shutter speed priority are 1/1000 s and 1/800 s for sunny and cloudy weather, correspondingly; (3) An aperture and ISO sensitivity values are selected automatically, one time per a flight line; and (4) Images are recorded as JPEG files.…”

Section: A Uas Characteristicsmentioning

confidence: 99%

“…In particular, UAS-derived imagery is utilized for producing high-resolution DEMs, which are then applied to model dynamics of slope, coastal, and fluvial processes, to reveal fine geological features, etc. [1].…”

Section: Introductionmentioning

confidence: 99%

The 2017 catastrophic subsidence in the Dålk Glacier: Unmanned aerial survey and digital terrain analysis

Florinsky

Bliakharskii

Попов³

et al. 2018

Preprint

Self Cite

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Abstract-We present the first results from a study of the 2017 catastrophic subsidence in the Dålk Glacier, East Antarctica using an unmanned aerial system (UAS) and UAS-derived DEMs. The subsided portion of the Dålk Glacier and adjacent territory was surveyed in two flights, before and after the collapse. The survey was performed by Geoscan 201, a small flying-wing UAS. Aerial images have an average resolution of 6 cm. Using Agisoft PhotoScan software, we generated two DEMs with a resolution of 22 cm related to the pre-and post-collapsed glacier surface. To model the pre-collapsed subglacial cavern, one DEM was subtracted from the other. Finally, we discuss a probable mechanism of the catastrophic subsidence.

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Geomorphometry from unmanned aerial surveys

Cited by 19 publications

References 58 publications

Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection

Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection

A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

The 2017 catastrophic subsidence in the Dålk Glacier: Unmanned aerial survey and digital terrain analysis

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