Commercial-UAV-Based Structure from Motion for Geological and Geohazard Studies

Bonali, Fabio Luca; Corti, Noemi; Russo, Elena; Marchese, Fabio; Fallati, Luca; Mariotto, Federico Pasquaré; Tibaldi, Alessandro

doi:10.1007/978-94-024-2046-3_22

Cited by 6 publications

(2 citation statements)

References 142 publications

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“…Commensurately, the rapid uptake of low-cost, readily deployable multi-senor drones has extended the reach of such techniques, enabling nadir view photogrammetric surveys of horizontal outcrops, as well as occlusion free reconstructions of large vertical sections [8][9][10]. Despite the relative simplicity with which 3D surface reconstructions of geological exposures (i.e., virtual or digital outcrop models: [11][12][13][14][15][16][17][18][19]) can be acquired using such platforms, the reliability of the geospatial information extracted from their data products is typically unclear, particularly when survey grade measurements are unavailable to calibrate and benchmark the resultant outcrop models. The deployed sensor platform's accuracy and precision in terms of position and orientation is often a key consideration for many geoscientific applications of 3D surface reconstructions, with deviations in the resultant scale, geolocation and attitude of the generated models being deleterious to the quality of metric data extracted thereof.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

et al. 2022

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We are witnessing a digital revolution in geoscientific field data collection and data sharing, driven by the availability of low-cost sensory platforms capable of generating accurate surface reconstructions as well as the proliferation of apps and repositories which can leverage their data products. Whilst the wider proliferation of 3D close-range remote sensing applications is welcome, improved accessibility is often at the expense of model accuracy. To test the accuracy of consumer-grade close-range 3D model acquisition platforms commonly employed for geo-documentation, we have mapped a 20-m-wide trench using aerial and terrestrial photogrammetry, as well as iOS LiDAR. The latter was used to map the trench using both the 3D Scanner App and PIX4Dcatch applications. Comparative analysis suggests that only in optimal scenarios can geotagged field-based photographs alone result in models with acceptable scaling errors, though even in these cases, the orientation of the transformed model is not sufficiently accurate for most geoscientific applications requiring structural metric data. The apps tested for iOS LiDAR acquisition were able to produce accurately scaled models, though surface deformations caused by simultaneous localization and mapping (SLAM) errors are present. Finally, of the tested apps, PIX4Dcatch is the iOS LiDAR acquisition tool able to produce correctly oriented models.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

et al. 2022

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“…It has been gaining diverse applications in the most varied disciplines, including landslide studies [17], for being low-cost (regarding both hardware and software), being easy to learn and use, and providing point density and accuracy comparable to that obtained via lidar (light detection and ranging). When paired with remotely piloted aircrafts (RPAs), it can be even more efficient [18,19], as RPA-based SfM-MSV has proven to be extremely powerful for hazard mapping and landslide-related data collection [17,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. For these reasons, RPA SfM-MVS is an excellent option for those applications that benefit from frequently revised terrain models for landslide risk monitoring.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models

et al. 2023

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This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU Paz” (CP) and “Parque Santa Madalena I” (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork.

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Method for Planning a Coverage Trajectory for a Group of UAVs Marking Out Zones for Installing Seismic Modules

Izhboldina

Iakovlev

Брыксин³

et al. 2022

Lecture Notes in Computer Science

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Commercial-UAV-Based Structure from Motion for Geological and Geohazard Studies

Cited by 6 publications

References 142 publications

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models

Method for Planning a Coverage Trajectory for a Group of UAVs Marking Out Zones for Installing Seismic Modules

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