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107 results for “digital elevation models”
High-resolution digital elevation model of Klados Gorge, Crete, Greece
<p>High-resolution digital elevation model constructed from photogrammetric processing of drone images taken at Klados Gorge, Crete, Greece in 2017. The authors used <em>Agisoft</em> PhotoScan for the photogrammetric processing and to generate 3D spatial data for further use.</p>
Orthomosaics and digital elevation model of the 'Bear Trap' - a Norse ruin in Northwest Greenland
<p>This dataset consists of a digital elevation model (DEM) and an orthomosaic of the ‘Bear Trap’ (also called ‘Bjørnefælden’ in Danish, and ‘Putdlagssuaq’ or ‘The Great Trap’ Greenlandic Kalaallisut), a Norse ruin at the western end of the Nuussuaq Peninsula in NW Greenland. Images comprise 1032 low-altitude aerial images acquired from an unoccupied aerial vehicle (DJI Phantom 3 Standard). These images were processed using Agisoft Metashape Pro (v1.7; Linux Ubuntu) following the USGS protocols for processing imagery in coastal areas. The locations of 8 ground control points (GCPs) were surveyed with a high accuracy global navigation satellite system (GNSS) receiver (Emlid Reach). The base station and rover data were processed using the Emlid distribution of the free RTKLib software (<a href="https://docs.emlid.com/reach/common/tutorials/gps-post-processing/">https://docs.emlid.com/reach/common/tutorials/gps-post-processing/</a>). Geoid height was computed using the online UNAVCO Geoid Height Calculator (<a href="https://www.unavco.org/software/geodetic-utilities/geoid-height-calculator/geoid-height-calculator.html">https://www.unavco.org/software/geodetic-utilities/geoid-height-calculator/geoid-height-calculator.html</a>). During the image alignment step in Metashape, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were not used. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the intrinsic camera parameters were computed for each camera calibration group. GCPs were then imported and placed in each image. The dense point cloud was then computed using the ‘Ultra High’ quality setting, followed by the DEM and orthomosaic. The resolution of the orthomosaic is 1.83 cm/pixel. 5 cm resolution orthomosaic and DEM were also exported for use in QGIS. </p> <p> </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p> </p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, in affiliation with the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper archaeological permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provide an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provide further details specific to The Bear Trap and surrounding archaeological contexts observed during the 2019 survey.</p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to <em>Arctic Anthropology</em>.</p>
Digital elevation models, ortho images and outlines of Yala Glacier, Langtang Valley, Nepal Himalaya
<p>Datasets related to article "Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya". The data includes three digital elevation models (DEM), two ortho images and five outlines of Yala Glacier between 1981 and 2015.<br> </p> <p>Description of files:<br> 1) DEM and ortho image</p> <p>- 1981Yala_dem_20_-10_bias_cor.tif: 10 m resolution digital elevation model derived from a map that was generated using ground phogogrammetry images that were acquired in 1981 (Yokoyama, 1984; Fujita and Nuimura).</p> <p>- 2007Yala_dem_0_-4_bias_cor.tif: 2 m resolution digital elevation model derived from 14 oblique photographs that were acquired by a private jet with handheld cameras in 2007.<br> - 2007Yala_ortho.tif: an ortho images derived by the same data in 2007.</p> <p>- 2015Yala_dem_0_0_bias_cor.tif: 1 m resolution digital elevation model derived from 519 photographs that were acquired by a UAV-based photogrammetric survey in 2015.<br> - 2015Yala_ortho.tif: an ortho images derived by the same data in 2015.<br> <br> 2) Glacier boundary (shapefile Files)</p> <p>- Yala_area_1981: <br> - Yala_area_2007:<br> - Yala_area_2009:<br> - Yala_area_2012:<br> - Yala_area_2015:<br> <br> <br> Please see the related journal article for details on datasets.<br> <br> Sunako, S., Fujita, K., Izumi, T., Yamaguchi, S., Sakai, A., & Kayastha, R. (2023). Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya. Journal of Glaciology, 69(274), 425-432. doi:10.1017/jog.2022.118<br> </p>
INCA-CH seamless nowcasting system: 1km digital elevation model
<p>Digital elevation model at 1km horizontal resolution used in the INCA-CH seamless nowcasting system (in Swiss coordinates CH03). For INCA-CH parameters see here: <a href="https://zenodo.org/record/6470725">INCA-CH set of data</a></p>
Drone orthomosaic and digital elevation model of the Atanikerluk archaeological site in NW Greenland
<p>This dataset consists of a digital elevation model (DEM) and an orthomosaic of the Atanikerluk archaeological site (NKAH 1724), located on the Nuussuaq Peninsula in NW Greenland near the settlement of Saqqaq. The DEM and orthomosaic were produced from 612 aerial images that were acquired by a DJI Phantom 3 drone. The images were processed using Agisoft Metashape Professional (version 1.7.1) following protocols established by the United States Geological Survey (USGS) for drone surveys of coastal regions (Over et al., 2021). Five ground control points were distributed throughout the survey area. The drone survey of the Atanikerluk site was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, in affiliation with the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper archaeological permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provide an overview of the archaeological surveys conducted during the VIMOA project.</p> <p>Over et al. (2021) Processing Coastal Imagery With Agisoft Metashape Professional Edition, Version 1.6—Structure From Motion Workflow Documentation. USGS Open File Report 2021-1039. doi:10.3133/ofr20211039. <a href="https://pubs.er.usgs.gov/publication/ofr20211039">https://pubs.er.usgs.gov/publication/ofr20211039</a></p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. Antiquity 94:e6 doi:10.15184/aqy.2019.230</p>
Coastal Digital Elevation Models and Transects of the Reef Island Fuvahmulah, the Maldives
<p>The data contains <strong>two Digital Elevation Models</strong> (DEMs) of the coastal zone in the south-east of <strong>Fuvahmulah, the Maldives</strong> (location: latitude -0.30° and longitude 73.43°). DEM files are in the file format <em>*.tif</em> (raster data georeferenced to WGS84). The DEMs contain elevation data on the area adjacent to the seaport. One DEM was measured in 2017, the other in 2019. The 2017 DEM is based on aerial imagery, recorded with the consumer-grade unmanned aerial vehicle (UAV, or drone) DJI Phantom 4 in 2017, while the 2019 DEM was recorded with a DJI Phantom 4 Pro. The data was then processed in <em>Agisoft Photoscan</em> with a Structure-from-Motion - MultiView Stereo algorithm (SfM-MVS).<br> <br> In addition, the data set contains <strong>elevation</strong> and <strong>location data</strong> of 4 transects from sections along the east coast of Fuvahmulah. The data is stored in <em>*.csv </em>files, containing longitude, latitude, height and distance.<br> <br> Finally, there are two further data files with <strong>gridded topographic and bathymetric data</strong>, ready for use in the depth-integrated <strong>Boussinesq type model <em>BOSZ</em></strong> (see Roeber and Cheung, 2012; doi.org/10.1016/j.coastaleng.2012.06.001). Currently, the numerical wave model, incorporates lateral wave makers, so that the elevation data needs to be rotated to account for different wave directions <span class="math-tex">\(\theta\)</span>. The bathymetry was recorded with a <em>Dr. Fahrentholz LituBox 15/200</em> and truncated to 200 meters water depth. The coastal topography results from coastal DEMs, while the island's mainland is set to about 2-3 meters (no elevation information was available for the island's inland area). The data was gridded and interpolated onto the grid with <em>The Generic Mapping Tools (GMT)</em>. The grid size is 7.5 x 7.5 meter.<br> The files are in <em>MATLAB® 5.0</em> file format <em>*.mat</em>, containing the variables '<em>length</em>' and '<em>width</em>' of the domain in meters, '<em>X</em>' and '<em>Y</em>' (computation grid <span class="math-tex">\(x, y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude), as well as '<em>BATHY</em>', being the elevation data for the X,Y grid. In addition, friction values as used in the model is stored in the variable '<em>Friction</em>', as well as the grid increments '<em>DX</em>' and '<em>DY</em>' (<span class="math-tex">\(\Delta x\)</span> and <span class="math-tex">\(\Delta y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude).</p> <p>Each folder contains either a README-file or Jupyter Notebook (Python 3). The Notebooks allow the user to access and view the files (Python 3 and Jupyter Notebook installation required)</p>
Digital Elevation Models of Shatter cones
<p>20 Digital Elevation Model of Shatter cones </p> <p>Haughton Dome (1 model using Helicon Focus, 1 Model from Laser Scanning)<br /> Jebal Waqf as Suwwan (1 model using Helicon Focus, 1 Model from Laser Scanning)<br /> Gosses Bluff (4 models using Helicon Focus)<br /> Serra da Cangalhia (1 model from Laser Scanning)<br /> Rochechouart (2 models from Laser Scanning)<br /> Steinheim Basin (3 models from Laser Scanning)<br /> Vargeão Dome (1 model from Laser Scanning)<br /> Vista Alegre (1 model from Laser Scanning)<br /> Vredefort (4 models from Laser Scanning)</p>
Posina Catchment Digital elevation model
<p>This is the DEM data set used in paper "Estimating the water budget components and their variability in a Pre-Alpine basin with JGrass-NewAGE" by Abera W., Formetta G., Borga M. and Rigon R. </p>
Shuttle Radar Topography Mission digital elevation models, data points, radiocarbon dates, and geochemical data for the Rub' al Khali Desert
Open the record for dataset details and reuse information.
Digital elevation model of differences of two debris flow event in Chutou gully
<p>We release two datasets that reflected the height alteration of channel deposits during the debris flow events, which occurred on 20 August 2019 and 17 August 2020 in Chutou gully, Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. The first dataset indicates the height alteration during the 2019 debris flow, whereas the second indicates the 2020 debris flow. The datasets can be used to analyse the deposit evolution during the debris flow events. The datasets can be viewed or edited through the ArcGIS software</p>
Context Camera digital elevation models for Aeolis Dorsa, Mars
<p>Mars topography dataset with Context Camera (CTX) digital elevation models produced using the Ames Stereo Pipeline at the Murray Lab at Caltech.</p>
1-km bed topography digital elevation model (DEM) of the Weddell Sea sector, West Antarctica
<p>We present a new bed elevation digital elevation model (DEM) of the Weddell Sea sector, West Antarctica. The DEM consists a total area of ~125,000 km<sup>2</sup> covering the Institute, Möller and Foundation ice streams and the Bungenstock Ice Rise with a 1 km spatial resolution. </p> <p>In order to produce the bed elevation DEM, ice thickness DEM was formed from the available radio-echo sounding (RES) data using the 'Topo to Raster' function in ArcGIS. The RES data used in this study were compiled from four main sources which are (1) Scott Polar Research Institute (SPRI) survey collected during several campaigns in the 1970s (Drewry, 1983), (2) British Antarctic Survey (BAS) airborne radar survey conducted during the austal summer 2006/07 (GRADES/IMAGE) (Ashmore et al., 2014). , (3) BAS airborne survey accomplished during the Institute and Möller Antarctic Funding Initiative (IMAFI) in 2010/2011 (Ross et al., 2012) and (4) Center for the Remote Sensing of Ice Sheet (CReSIS) data during the NASA Operation IceBridge (OIB) programme in 2012, 2014 and 2016 (Gogineni, 2012). The ice thickness picks were gridded at a uniform 1-km spacing using the Nearest Neighbour interpolation within the Topo to Raster. The ice thickness DEM was later subtracted from the 1-km ice-sheet surface elevation derived from the combined European Remote Sensing Satellite-1 (ERS-1) radar and Ice, Cloud and land Elevation Satellite (ICESat) laser satellite altimetry DEM (Bamber et al., 2009), to produce the bed topography referenced to the Polar Stereographic projection (Snyder, 1987). </p>
Pléiades Digital Elevation Models of South Lhonak Lake (Sikkim, India)
<h1>Data and methods</h1> <p>On 3 October 2023, the collapse of a frozen lateral moraine into South Lhonak Lake triggered a multi-hazard cascade in the Sikkim Himalaya, India (Sattar et al, 2025). This repository contains two DEMs derived from Pléiades satellite images that were acquired before and after the event. </p> <ul> <li>A post-event DEM computed from a pair of PHR1A stereoscopic images acquired on 2023-10-29 (20231029.tif)</li> <li>A pre-event DEM computed from a triplet of PHR1A stereoscopic images acquired on 2022-10-18 (20221018.tif)</li> </ul> <p>The DEMs (height above the WGS84 ellipsoid) were posted at 1.0 m with UTM 45N projection (EPSG:32645) and coregistered to the GLO30 Copernicus DEM. They were computed with the MicMac software in the DSM-OPT service using the following configuration: correlation window size: 3x3, regularization factor: 0.05, vertical uncertainty: 0.1, potential accuracy threshold: 0.4 (Rupnik et al. 2017).</p> <p>These DEMs allow the analysis of topographic changes in the lake area. We included examples of such analysis with the addition of the High Mountain Asia 8 m DEM (Shean 2017).</p> <h1>Acknowledgements </h1> <p>Pléiades images were acquired and processed thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure.</p> <h1>References</h1> <div> <p>Sattar et al. (2025) The Sikkim flood of October 2023: Drivers, causes and impacts of a multihazard cascade. Science, eads2659, https://doi.org/10.1126/science.ads2659</p> Rupnik, E., Daakir, M., & Pierrot Deseilligny, M. (2017). MicMac–a free, open-source solution for photogrammetry. <em>Open geospatial data, software and standards</em>, <em>2</em>, 1-9. https://doi.org/10.1186/s40965-017-0027-2</div> <div> </div> <div>Shean, D. (2017). High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/KXOVQ9L172S2. Date Accessed 07-29-2024.</div>
Spot6 and Pléiades Digital Elevation Models (DEMs) and elevation change grid to understand the South Lhonak lake (Sikkim) flood of October 2023
<p>===========================</p> <p>Introduction</p> <p> </p> <p>On 3 October 2023, a multi-hazard cascade in the Sikkim Himalaya, India, was triggered by the collapse of a frozen lateral moraine into South Lhonak Lake</p> <p>We share here the SPOT6 and Pléiades DEMs and the elevation change maps derived from these images. The collection includes four files:</p> <p>Two pre-event DEMs from SPOT6</p> <p>2018-12-01_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>2018-12-08_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>One post-event mosaic DEM from Pléiades </p> <p>Post-Event_DEM_SGM_october2023.tif</p> <p>One grid of elevation change from 2018 to 2023</p> <p>dh_mosaic_Lhonak_SGM_median.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The SPOT6 and Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 4x4 m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 45N (EPSG:32645)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades and SPOT6 images were acquired thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p>
Mosaicked Digital Elevation Models over Nepal
<p>The DEMs in this dataset are generated by mosaicking four individual DEM tiles over the 2015 Nepal earthquake epicentral region. The mosaicked DEMs are available in the WGS84 vertical datum. The DEMs represent surface elevation before the occurrence of the 2015 earthquake event. The DEMs are used for topographic phase removal in the Differential InSAR studies.</p>
Seismic Inverted Impact force and Digital Elevation Models before and after the 2018 Baige Landslide
<p>For the data of inverted seismic force, the seismic signals recorded by the broadband seismic stations were prepared for the inversion of the force-time function by the following series of actions:</p> <ul> <li>Removing the instrumental response</li> <li>Resampling to 0.5 s</li> <li>Integrating the signals from velocity to displacement</li> <li>Rotating the horizontal components to the radial and transverse direction</li> <li>Filtering the signals between periods of 30 and 140 s</li> </ul> <p>For the digital elevation models (DEMs) of the Baige landslide, the pre-failure DEM with 10 m grid spacing was obtained from the Sichuan Bureau of Surveying Mapping and Geoinformation (SBSMG). At the same time, the post-failure DEM was derived from the UAV-based photogrammetry.</p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip". All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.</p> <p> Potential application of the presented dataset include:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p>The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617". </p>
High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia
<p>This dataset contains the inputs used for Structure from Motion Multiview Stereo photogrammetry processing for the year 1967 and 1984 i.e Unprocessed scanned historical aerial Photographs, camera position coordinates, flight index and Ground Control Points. All the scanned historical aerial photographs data are in TIFF format except four photographs in JPEG format under a zipped folder ("1967_Scanned_HAPs_Part1.7z and 1967_Scanned_HAPs_Part2.7z" for the 1967 Historical Aerial Photographs and "1984_Scanned_HAPs_Part1.7z and 1984_Scanned_HAPs_Part2.7z" for the 1984 Historical Aerial Photographs). The "Flight_Index.Zip" contains shapefiles of the camera position and polygon of consecutive aerial photograph index; "GCP.Zip" contains text file of the GCPs used for the 1967 and 1984; and "Camera_Position.Zip" contains the file of the camera position (Label, Easting, Northing and Altitude) of each historical aerial photographs. </p> <p>The results of the above dataset could be accessible on Zenodo repository "10.5281/zenodo.7269999".</p> <p>Anyone can reuse the presented dataset to produce DEMs and Orthomosaics; and use for the following application areas:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p> </p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
Digital Elevation Models (DEMs) after the Shovi (Caucasus) debris flow, 13 August 2023
<p>===========================</p> <p>Introduction</p> <p>On 4 August 2023, a large debris flow struck the mountain resort town of Shovi in Georgia. More on this event here : <a href="https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia">https://eos.org/thelandslideblog/the-4-august-2023-debris-flow-at-shovi-in-georgia</a></p> <p>Two Pléiades stereo images were acquired after the event on 13 August 2023, thanks to the activation of the CIEST² (<a href="https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/">https://www.poleterresolide.fr/ciest-2-nouvelle-generation-2/</a>) scheme. 2023-08-13a covers the lower part, 2023-08-13b the upper/source area.</p> <p>We share here the Pléiades DEMs derived from these images. The collection includes four files:</p> <p>Shovi_2023-08-13a_DEM_2m.tif</p> <p>Shovi_2023-08-13a_DEM_20m.tif</p> <p>Shovi_2023-08-13b_DEM_2m.tif</p> <p>Shovi_2023-08-13b_DEM_20m.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 2x2m and 20x20 m GSD and an orthoimage in 0.5x0.5m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the block matching algorithm and the set of processing parameters from Marti et al. (2016)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Marti, R. et al.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10(4), 1361–1380, doi:10.5194/tc-10-1361-2016, 2016.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 38N (EPSG:32638)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p>Pléiades dataset includes only DEMs because a licence needs to be signed with CNES to access Pléiades imagery. Please contact the authors for further information on this.</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades images were provided under the CIEST² initiative (CIEST2 is part of ForM@Ter (https://en.poleterresolide.fr/) (Pléiades © CNES 2023, distribution AIRBUS DS)</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p> <p>===========================</p> <p>Citation:</p> <p>Please cite this repository as :</p> <p>Berthier E. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023, Dataset distributed on Zenodo: 10.5281/zenodo.8252339</p>
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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.