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30 results for “Structure from Motion”
Dataset for "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion"
<p>This repository contains the data of the simulations and theoretical<br>calculations of the paper "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion".</p>
Supplement - Structure from Motion Raster Data
<p>We created orthorectified images and digital elevation models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this repository. </p>
Multi-temporal Structure from Motion ponit clouds of riparian vegetation
<p>the dataset consists of three pointclouds and two NIR orthomosaics generated through a Structure from Motion standard workflow of the same forested area. The study area is typical riparian habitat vegetation. The data were acquired in different phenological stages:</p> <p>the first acquisition was realised in leaves-off conditions (march 2020)</p> <p>The second acquisition was realised in June 2020</p> <p>the third acquisition was realised in July 2020.</p> <p>Reference system: WGS84/32N [EPGS: 32632]</p> <p>For further information regarding the data processing please refer to https://doi.org/10.3390/rs13091756<br> </p>
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
Supplement - Structure from Motion Metadata and Outcomes
<p>Ground control points used to ensure Structure from Motion (SfM) terrain models were georectified (Westoby et al. 2012; Wolf 2021) using Emlid R2 RTK (real-time kinematic) GNSS (global navigation satellite system) system consisting of a base station set up over an established known point (established with Canadian Geodetic Survey of Natural Resources Canada (NRCAN) service Canadian Spatial Reference System Precise Point Positioning (CSRS- PPP)) and a rover. </p> <p>Once the ground control points were surveyed, aerial drone images were acquired. We created flight polygons in Drone Deploy. Pictures were captured with a DJI Mavic II drone with minimum 80 % overlap of photos. Drone Deploy was chosen because it has an option to account for the doming error commonly found in models created from drone imagery and structure from motion (SfM). The doming effect is a systematic error that impacts the DEMs vertical component and can provide errors larger than the usual centimeter level (Sanz-Ablanedo et al. 2020). Generally, each site was flown once in fall of 2020 and once in spring of 2021. </p> <p>We created orthorectified images and digital terrain models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this Zenodo repository.</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Chateau Champs - very long focal length dataset for Structure from Motion algorithms
<p>This dataset contains a photogrammetric acquisition (99 images) of a sculpture head located in Château de Champs-sur-Marne, France. The images were taken with the full-frame Canon EOS 5D Mark II and a focal length of 1000mm.</p>
In-scanner head motion and structural covariance networks
Open the record for dataset details and reuse information.
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.
Buddha - Structure-from-Motion
Sparse reconstruction result after the Structure-from-Motion step in Meshroom default photogrammetry pipeline. The green points represent the reconstructed cameras. **Details** - Meshroom 2019.1.0 - Input: 220 images - Camera: Sony A7S II Source: Objaverse 1.0 / Sketchfab
Monitoring creep along the Hayward Fault using structure-from-motion photogrammetry of offset curbs"
<p>Point clouds for each observed offset curbs along the Hayward Fault in Fremont, California between 2016 to 2018. </p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
Optimized structures for Optical control of ultrafast structural motion in a fluorescent protein
<p>QM-MM Optimized structures of the<strong> </strong>hydrogen bonding configuration in states A1, A2 and Transition State (TS) between them for rsKiiro protein on ground (s0) and first excited (s1) states. Structures were optimized at PBE0-D3/cc-pVDZ//Amber03 level.</p>
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
Open the record for dataset details and reuse information.
High resolution shallow structure of Ebao basin revealed with DAS ambient noise tomography and its relation to earthquake ground motion
<p>EBAO dataset: fig2b, fig3a, fig9, figS5</p>
02, A _Statue 1 (structure from motion)
Source: Objaverse 1.0 / Sketchfab
Data from: Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes
Open the record for dataset details and reuse information.
Data from: Structure from motion photogrammetry: does the choice of software matter for Ecology?
Structure-from-Motion (SfM) and Multiview-Stereo (MVS) is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM+MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM+MVS if it is to fulfil a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimetre-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM+MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software – Photoscan, Pix4D, 3DFlow Zephyr and MICMAC, were compared with in-situ sward height data captured on the same day as image capture. Using the same replicate image dataset (n=3) as input we demonstrate that there are 1.7, 2.0 and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM+MVS software using "High", "Medium" and "Low" quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n=3) processed using the same SfM+MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5 and 2.7 cm (excluding one outlier) for "High", "Medium" and "Low" quality settings, respectively. We conclude that SfM+MVS software choice does matter.
Structure from motion of the Ichilo riverbanks in Puerto Villarroel, Bolivia
<p>Structure from motion photogrametry was used to survey the banks of the Ichilo river, in the city of Puerto Villarroel in Bolivia. The surveyed areas was of aproximately 300 hectares of terrain. A DJI Phantom 4 Pro and a DJI Mavic Pro was used for taking pictures. A total of 44 GCPs were marked along the riverbanks of the Ichilo and their coordinates were measured using a Real-time Kinematic (RTK) GPS unit. After the GCPs were marked, the UAVs were launched and the pictures were taken including the GCPs and a total of 1358 pictures were taken during the surveys. The imagery was analysed and processed using the software Agisoft PhotoScan and orthophotos. The GCPs were split in in two groups: the first group with thirty GCPs were used to correct the imagery, and the other 14 were used to assess the accuracy of the imagery. The processed imagery taken in May, 2019 resulted in a orthomosaic and a digital elevation model (DEM). Also, particle size distributions of two samples taken in the the Ichilo river are presented.</p>
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