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323 results for “Photogrammetry”
Shadow Neural Radiance Fields for Multi-View Satellite Photogrammetry - Dataset
<p>Data accompanying the paper Derksen, Dawa, and Dario Izzo. "Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry." <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em>. 2021.</p> <p>This dataset contains a subset of the World-View-3 images from the IEEE Data Fusion Competition 2019 - Track 3, downloaded from https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019.</p> <p>It is organized in four folders, one for each study area, following the original area names. Each folder contains a multi-view set of RGB images, cropped to the validation area, and rotated according to the azimuth angle. The folder also contains a Digital Surface Model of the area (DSM) as a one-band .tif file where the values contained in pixels represent the surface altitude in meters. Finally the folder contains a "metadata" file which provides for each image ID the radius (distance from satellite to scene), as well as the viewing and lighting directions (azimuth and elevation).</p> <p>The authors would like to thank the Johns Hopkins University Applied Physics Laboratory and IARPA for providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the Data Fusion Contest.</p>
A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry (Data)
<p>This is the underlying data for the publication "A Non-destructive Method to Create a Time Series of Surface Area for Coral Using 3D Photogrammetry" by Daniel D Conley and Erin N. R. Hollander published in 2021.</p>
Automated, high-throughput image calibration for parallel-laser photogrammetry
<p>This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.</p> <p>Paper abstract: <span>Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. </span></p>
Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
<p>We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-ranging animals simultaneously. Pinnipeds reliably haul-out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations.</p> <p>UAS imagery of grey seals (<i>Halichoerus grypus</i>) was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic, and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n=21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n=32 [24]; pups: n=33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.</p> <p>UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean-absolute-error, adult female: 8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.</p> <p>The method was applied to 673 free-ranging animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (mean±SE: 9.2±1.2) were modeled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea-ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, and the ability to measure whole groups of free-ranging animals at once makes strides towards 'weighing populations'.</p>
Reconstructed Aneto glacier surfaces from historic aerial image photogrammetry (1981) and remote sensing techniques (2020, 2021, 2022)
<p>The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. </p>
Supplementary material to: Using UAV-based photogrammetry coupled with in-situ fieldwork and U-Pb geochronology to decipher multi-phase deformation processes: A case study from Sarclet, Inner Moray Firth Basin, UK.
<p>This dataset represents the supplementary material to: Using UAV-based photogrammetry coupled with in-situ fieldwork and U-Pb geochronology to decipher multi-phase deformation processes: A case study from Sarclet, Inner Moray Firth Basin, UK.</p> <p>Appendix A - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘The Haven and The Stack’ locality in 3D PDF format.<br> Appendix B - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘Muiri Geo’ lo-cality in 3D PDF format.<br> Appendix C - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘The Haven and The Stack’ locality in .obj format.<br> Appendix D - This appendix represents the 3D Digital Outcrop Modell (DOM) of Sarclet ‘Muiri Geo’ lo-cality in .obj format.<br> Appendix E - This appendix represents the orthomosaic of Sarclet ‘Muiri Geo’ locality in GeoTiff format.<br> Appendix F - This appendix represents the orthomosaic of Sarclet ‘Muiri Geo’ locality in GeoTiff format.<br> Appendix G - This appendix represents the analytical conditions and geochronology data of the studied samples.</p>
Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry
<p><span>Understanding the population health status of long-lived and slow-reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population-level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age-structure). We tested a novel and frequentist approach, using Unoccupied Aerial System- (UAS) photogrammetry, to assess the population age-structure of small delphinids. First, we measured the precision and accuracy of UAS-photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (<em>Tursiops</em> <em>truncatus</em>). Using a log-transformed linear model, we estimated TL using the blowhole-to-dorsal-fin-distance (BHDF) for surfacing animals. To test the performance of UAS-photogrammetry to age-classify individuals, we then used length measurements from a 35-year dataset from a free-ranging bottlenose dolphin community to simulate UAS-estimates of BHDF and TL. We tested five age-classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS-simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS-estimated BHDF. Our age-classifiers performed best in predicting age-class when using broader and fewer (two and three) age-class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5-93% of the individuals were correctly classified within two years of their actual age-class bin. Similar classification performances were obtained using both proxies. UAS-photogrammetry is a non-invasive, inexpensive, and effective method to estimate TL and age-class of free-swimming dolphins. UAS-photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.</span></p>
Raw Data Examples for Close-Range Photogrammetry of Replicative Experiments on Ground Stone Tools
<p>The data presented in this study pertains to the experimental collection built to verify the applicability of close-range photogrammetry to ground stone tools (GSTs).</p> <p>GS17 is a sandstone slab collected from the Fiora River in Manciano, Italy, where sandstone of Miocene formation outcrops. This stone is primarily composed of quartz grains embedded in abundant carbonate cement. It was utilized as a GST in replicative experiments, paired with an active tool from the same provenience, and subjected to 2 hours of<em> Rumex crispus</em> achenes grinding. Photogrammetric techniques based on Structure-from-Motion and Multi-View Stereo reconstruction were employed to generate the 3D models of the tool at different stages of the replicative use. We recorded the geometry of the GST before (referred to as T<sub>0</sub>) and after use (T<sub>4</sub>) to later compare and assess the change in object geometry during the transformation of vegetal resources, thereby facilitating our understanding of archaeological tools.</p> <p>This dataset comprises the pictures required for the elaboration of the GS17 models at T<sub>0</sub> and T<sub>4</sub>, as well as the data necessary for calibration.</p>
Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity
<p>This is data for the paper "Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity". (<strong>DOI: </strong><a href="https://doi.org/10.1002/esp.5665">https://doi.org/10.1002/esp.5665</a>)</p> <p>This data is openly available, provided the original work is properly cited. The unzip Password can be found on the original paper in ESPL.</p>
Leopard 1A4 [photogrammetry scan]
This 3D model was generated with the photogrammetry software 3DF Zephyr v4.523 processing 989 images taken from several iPhone XS and Nikon D3500 videos. **This model is to be used for reference when making 3D models of this tank** The **Leopard (or Leopard 1)** is a main battle tank designed and produced by Porsche in West Germany that first entered service in 1965. Developed in an era when HEAT warheads were thought to make conventional heavy armour of limited value, the Leopard focused on firepower instead of armor. The **Leopard 1A4** formed the sixth batch of 250 vehicles, delivery starting in 1974. The 1A4 was externally similar to the 1A3, but included a new computerized fire control system and the new EMES 12A1 sighting system to aim it. In addition, the commander was provided with his own independent night sighting system, the PERI R12. The new equipment used up space and the ammunition load was reduced to 55 rounds, of which 42 were stored in the magazine to the left of the driver. Source: Objaverse 1.0 / Sketchfab
Firehouse - DJI Mini 2 fly by (photogrammetry)
Using 3D scanning to preserve historical sites and make them available in a digital format is one of our favorite things about living in the future. We collaborated on this scan, using Matterport to capture the inside, and a DJI Mini 2 drone + ObjectCapture to capture the outside. This historical firehouse can be found in Stevens Point WI, and now you can tour it from the comfort of your living room - let us know what you think! See the [inside](https://my.matterport.com/show/?m=YpW6KieYppC&fbclid=IwAR1Mz5y7b-fsXJPsJz5Qgw20WhDRRqwP50G9TrKZi6r1NoE1-bgBqOaXZBs) (credit: [Calnan Design Group](https://www.calnandesign.com/)) And the outside, (see scan above, credit: [Spectre3D](https://www.spectre3d.io/)) Follow us on [Twitter](https://twitter.com/Spectre_3D) Source: Objaverse 1.0 / Sketchfab
Castle wall in Czorsztyn [free photogrammetry]
A photogrammetry model - fragment of castle wall in Czorsztyn [Chorshtyn], Poland :) Fairly optimized - comes from my old collection of photos so I enabled the free download for it :) Source: Objaverse 1.0 / Sketchfab
BMP-2 photogrammetry 3D scan
This 3D model was generated with the photogrammetry software 3DF Zephyr v4.530 processing 683 images of which abuot 430 where DSLR RAW images and the rest being from 2 iPhone videos. **This 3D model is to be used as reference for 3D artists.** Originially created for [Gunner, HEAT, PC!](https://www.gunnerheatpc.com/)(GHPC). The BMP-2 (Boyevaya Mashina Pekhoty, Russian: Боевая Машина Пехоты, literally "infantry combat vehicle") is a second-generation, amphibious infantry fighting vehicle introduced in the 1980s in the Soviet Union, following on from the BMP-1 of the 1960s. This specific BMP-2 was used by the NVA the army of East Germany.  Source: Objaverse 1.0 / Sketchfab
#15 - Angel Of Death: Medium+Photogrammetry
Day 15 of Everyday a Scan! Original Photogrammetry mesh based on: https://sketchfab.com/models/e2052b08ed6a455699ddb533ef29c277 Sculpted in Oculus Medium Source: Objaverse 1.0 / Sketchfab
Snowy New Hampshire House (Photogrammetry)
Created in RealityCapture by Capturing Reality from 238 images in 00h:26m:26s. This one was done with a DJI mavic and the results are pretty good for their being so much snow Need to cut down some trees to get a better scan. This house was built in the 1800s Scan by austin beaulier Source: Objaverse 1.0 / Sketchfab
Fire Hydrant - Photogrammetry
This model was generated using Apple's photogrammetry API. 400 snaps were taken through the capture app. Approximately 3.5 hours of processing time with "unordered" mode. Source: Objaverse 1.0 / Sketchfab
Basalt organs (photogrammetry)
# Photogrammetry of Basalt Organs This 3D model was realised in Reunion Island in "l'Entre-Deux" <br> <br> <strong> Camera: </strong> Sony DSC HX-100V <br> <strong> Type: </strong> Photogrammetry <br> <strong> Focal: </strong> 24 x 36 (Source of Wikipedia): Organs (by analogy with the instrument) or basalt columns are a geological formation made up of regular columns. It results from the solidification and thermal contraction of a basaltic flow shortly after its emission. The lower part, which cools or dries up more slowly, fractures from surface to depth in the form of sub-vertical prisms with a hexagonal section of decimetric order. These columns are surmounted by a zone of small less regular prisms (or "false prism") which can be combined in sheaves. Basaltic organs in the arm of the L'Entre-Deux river. By extension one often qualifies as basaltic organs volcanic formations whose composition is not basaltic, for example in France the organs of Bort and of the Sanadoire rock (made up of phonolite). Source: Objaverse 1.0 / Sketchfab
Aereal 360 RAW photogrammetry. Virgen Panecillo
Fotogrametría en crudo de la Virgen del panecillo en 3D. La versión HD refaccionada y pulida no está subida todavía. Source: Objaverse 1.0 / Sketchfab
High wall - photogrammetry
Quick and dirty scan created with Polycam of a high wall located in the park of Paris Observatory. Source: Objaverse 1.0 / Sketchfab
Rabbit on a tree trunk Photogrammetry Scan
3D scan of a rabbit on tree trunk sculpture from Heartwood forest, taken using 63 photos and a lumix S5 processed in Agisoft Metashape and cleaned with Blender/Zbrush. Free to use in your projects but pls credit me and the sculptor. Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.