Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
107
datasets available to search
ShareScore release 0.9.0
Dataset results
107 results for “digital elevation models”
Digital Elevation Model of Hintereis- and Kesselwandferner 08.10.2010
<p>DTM_RegionHintereis_2010_r1.tif: DEM of Hintereis- and Kesselwandferner, acquisition date 08.10.2010 by airborne laser scan (ALS), cellsize 1x1 m, UTM WGS84, ellipsoid heights<br> DTM_RegionHintereis_2010_r1_gc.tif: Same as above but corrected for geoid heights.</p> <p>These DEMs are a 1x1 m version of the 10x10 m DEM from 08.10.2010 available on https://doi.org/10.1594/PANGAEA.875889</p> <p>Reference: Bollmann et al. 2015: https://onlinelibrary.wiley.com/doi/10.1002/ppp.1852</p> <p>Data Source / Acknowledgement: The ALS data were provided by the Institute of Geography, University of Innsbruck, Austria, financed by the project ‘Multiscale Snow/Icemelt Discharge Simulation into Alpine Reservoirs’ (MUSICALS).</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.</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>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 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.<br><br>===========================</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>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>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>Citation:</p><p>Please cite this repository as described below:</p><p>Etienne Berthier. (2023). Digital Elevation Models (DEMs) after the Shovi flood (Caucasus), 13 August 2023</p>
Digital Elevation Model (DEM) raster layer for interior Alaska
This is a raster file in .e00 file that has a number of values that represent a range of elevations across Interior Alaska.
10m Digital Elevation Model From National Elevation Dataset, Niwot Ridge LTER Project Area, Colorado
The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are: Geographic coordinate system Horizontal datum of NAD83, except for AK which is NAD27 Vertical datum of NAVD88, except for AK which is NAVD29 Z units of meters NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
10m Digital Elevation Model Shaded Relief From National Elevation Dataset, Niwot Ridge LTER Project Area, Colorado
The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are: Geographic coordinate system Horizontal datum of NAD83, except for AK which is NAD27 Vertical datum of NAVD88, except for AK which is NAVD29 Z units of meters NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
30m Digital Elevation Model From National Elevation Dataset, Niwot Ridge LTER Project Area, Colorado
The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are: Geographic coordinate system Horizontal datum of NAD83, except for AK which is NAD27 Vertical datum of NAVD88, except for AK which is NAVD29 Z units of meters NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
30m Digital Elevation Model Shaded Relief From National Elevation Dataset, Niwot Ridge LTER Project Area, Colorado
The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are: Geographic coordinate system Horizontal datum of NAD83, except for AK which is NAD27 Vertical datum of NAVD88, except for AK which is NAVD29 Z units of meters. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
LiDAR-based Digital Elevation Model for Northampton and Accomack Co., VA, 2010
This dataset contains a bare-earth digital elevation model (DEM) for Northampton and Accomack Counties, Virginia based on data collected March 25-30, 2010 and processed to yield bare-earth elevations. It was created using LiDAR by Sanborne Geosystems under a contract with the Virginia Information Technologies Agency (VITA) with funding from The Nature Conservancy, USGS and the Virginia Coast Reserve Long-term Ecological Research project of the University of Virginia. Original LiDAR point data (approximately 1 meter spacing) was used to create a digital elevation model (DEM) with a cell resolution of 10 ft. (3.048 m). The DEM data layer is in the State Plane coordinate system (U.S. Feet) and uses the NAVD88 vertical datum with the 2009 Geoid for elevation in feet. As detailed in the included quality report, elevations are accurate to 0.65 feet or better. Water areas have been hydroflattened and may also include salt marsh areas that were inundated at the time of the flights. As a result, water areas were given a default minimum elevation below the minimum elevation measured by LiDAR over a given area and dependent on tidal cycle. Areas at or below this minimum elevation within the water mask may include salt marsh and tidal flats as well as open water. The elevation of the hydroflattening varies spatially. Salt marsh areas within the water mask were later determined by VCRLTER staff based on the following factors: (1) present as marsh (code 18) in the NOAA CCAP 2006 land cover layer and not included as an open water feature in the 2010 USGS National Hydrography Dataset, (2) minimum contiguous area of 1800 square meters, approximately equal to two 30 meter resolution CCAP pixel cells, and (3) includes extensive areas of salt marsh within north-south flight line “stripesâ€, primarily the seaside lagoons and marshes south of Parramore Island and the town of Wachapreague plus Chesapeake Bay marshes immediately north of Tangier Island (and excludes edge-only areas elsew
Digital Elevation Models for use with Fogpy
<p>Digital Elevation Models (DEMs) in geotiff format. One for central Europe and one for northeast USA. </p> <p><strong>Data sources</strong></p> <p>Central Europe: <a href="https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1">EU-DEM v1.1 (Copernicus Land Monitoring Service)</a></p> <p>USA: <a href="https://viewer.nationalmap.gov/datasets/">USGS National Map</a></p> <p><strong>Data processing</strong></p> <p>Data were processed with GDAL. After collecting the required source files in geotiff, the final file was produced with two steps:</p> <pre>gdal_merge.py -o merged.tif */*.tif</pre> <pre>gdalwarp -r bilinear -t_srs '+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs +type=crs' -tr 500 500 merged.tif merged-2-500.tif</pre> <p><strong>Context</strong></p> <p>These elevation models are needed by <a href="https://github.com/pytroll/fogpy">Fogpy</a>, a satellite fog retrieval package within the Pytroll satellite processing set of tools.</p>
SPOT5 (2007), ASTER (2016) and SPOT7 (2018) Digital Elevation Models of Little Kluane Glacier (Yukon Territory)
<p>===========================</p> <p>Authors</p> <p>Etienne BERTHIER</p> <p>LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France,</p> <p>===========================<br> 1. Collection</p> <p>This collection contains three digital elevation models (DEMs) of "Little Kluane" Glacier (Yukon Territory, Canada) with an horizontal grid spacing of 30 m<br> * SPOT5, 13 September 2007<br> * ASTER, 30 September 2017<br> * SPOT7, 1 October 2018</p> <p>The original SPOT5 DEM was obtained from the SPIRIT project (Korona et al., 2009)<br> ASTER and SPOT7 DEMs have been derived using the Ames Stereo Pipeline (Beyer et al., 2018) from stereo images using the set of correlation of parameters in Deschamps-Berger et al. (2020)</p> <p>All DEMs have been coregistered and bias-corrected to the Copernicus 30 m global DEM following the methods of Berthier & Brun (2019)</p> <p><br> ===========================</p> <p>2. Dataset Acknowledgement</p> <p>* SPOT5 data were obtained thanks to funding from CNES during the fourth international polar year.<br> * SPOT7 data were obtained thanks to DINAMIS Project, for “Dispositif Institutionnel National d’Approvisionnement Mutualisé en Imagerie Satellitaire”, a French platform that acquires and distributes very high resolution Earth satellite imagery for French and foreign institutional users under specific subscription conditions.<br> * ASTER data were provided by NASA (U.S.) and METI (Japan).</p> <p>===========================</p> <p>3. Dataset Attribution</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p><br> ===========================</p> <p>4. Related publication</p> <p>This dataset has been generated for and used in a publication to be submitted to the Journal of Glaciology :<br> Morin, A., Flowers, G. E., Nolan, A., Brinkerhoff, D. J., and Berthier, E.: Exploiting high-slip ?ow regimes to improve bed inference, submitted.<br> </p> <p>===========================</p> <p>5. Collection Location</p> <p>Yukon Territory of Canada<br> Bounding box: WGS 84 / UTM zone 7N</p> <p><north>6771585.730</north><br> <south>6739845.730</south><br> <east>564391.250</east><br> <west>601261.250</west></p> <p><br> ===========================</p> <p>References</p> <p>Berthier, E. and Brun, F.: Karakoram geodetic glacier mass balances between 2008 and 2016: persistence of the anomaly and influence of a large rock avalanche on Siachen Glacier, J Glaciol, 65, 494–507, https://doi.org/10.1017/jog.2019.32, 2019.<br> 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.<br> 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.<br> Korona, J., Berthier, E., Bernard, M., Remy, F., and Thouvenot, E.: SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies during the fourth International Polar Year (2007-2009), ISPRS J. Photogramm., 64, 204–212, https://doi.org/10.1016/j.isprsjprs.2008.10.005, 2009.</p> <p> </p>
Data from: Very high resolution digital elevation models: are multi-scale derived variables ecologically relevant?
Open the record for dataset details and reuse information.
Data from: LiDAR-derived digital elevation model of Whale's Tail Marsh, San Francisco Bay, 2019
Open the record for dataset details and reuse information.
Database of Geographic Information: Hill shade, of the 1:250000 scale Digital Elevation Model of Arizona
This data set is a hill shade, of the 1:250000 scale Digital Elevation Model of Arizona. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). A 30-minute DEM (2- by 2-arc second data spacing) consists of four 15-by 15-minute DEM blocks. Two 30-minute DEM's provide the same coverage as a standard USGS 30- by 60-minute quadrangle. Saleable units are 30- by 30-minute blocks, that is, four 15- by 15-minute DEM's representing one half of a 1:100,000-scale map.
Digital Elevation Model (AZ 250,000:1)
1:250000 scale Digital Elevation Model of Arizona. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). A 30-minute DEM (2- by 2-arc second data spacing) consists of four 15-by 15-minute DEM blocks. Two 30-minute DEM's provide the same coverage as a standard USGS 30- by 60-minute quadrangle. Saleable units are 30- by 30-minute blocks, that is, four 15- by 15-minute DEM's representing one half of a 1:100,000-scale map.
PIE LTER 2005 Digital elevation model for the Plum Island Sound estuary, Massachusetts, filtered_grd, last filtered grid - Raster
On April 18 and 19, 2005 LIDAR (Light Detection and Radar) flights occured in the lower portion of the Plum Island Sound, Massachusetts estuary and were timed to correspond to low tides to minimize higher tide influences on pulsed returns. The LIDAR survey was conducted by the National Center for Airborne Laser Mapping (NCALM). This data set consists of a one meter Digital Elevation Model (DEM) in ESRI GRID file format based upon last filtered grid (bare-earth) data from the LIDAR flights.
LROC NAC-based high-resolution (2 m per pixel) digital elevation models (DEMs) of twelve regions on the Moon containing ring-moat dome structures (RMDSs)
<p>The DEMs were constructed based on Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images using shape from shading constrained by lower-resolution stereo data. They have been introduced in the following publication, where more details can be found:</p> <p> </p> <p>F. Zhang, J. W. Head, C. Wöhler, R. Bugiolacchi, L. Wilson, A. T. Basilevsky, A. Grumpe, Y. L. Zou.</p> <p>Ring-Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin.</p> <p>Journal of Geophysical Research: Planets, 125, e2019JE005967. https://doi.org/10.1029/2019JE005967</p> <p> </p> <p>The file format of the DEMs is GEOTIFF, the pixel values indicate evelations in meters. The DEMs are numbered as in Table 1 of the paper.</p>
Digital elevation models of terrain and landslide body used for simulation of Liegang landslide (V1)
Open the record for dataset details and reuse information.
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Anaheim, CA Digital Elevation Models
Open the record for dataset details and reuse information.
Digital Elevation Model (AZ 7.5 - Minute)
7.5 Minute Digital Elevation Model for the state of Arizona. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. The DEM data are stored as a series of profiles in which the spacing of the elevations along and between each profile is in regular whole number intervals. The normal orientation of data is by columns and rows. Each column contains a series of elevations ordered from south to north with the order of the columns from west to east. The DEM is formatted as one ASCII header record (A-record), followed by a series of profile records (B-records) each of which include a short B-record header followed by a series of ASCII integer elevations per each profile. The last physical record of the DEM is an accuracy record (C-record). The DEM for 7.5-minute units correspond to the USGS 1:24000 scale topographic quadrangle map series for all of the United States and its territories. Each 7.5 minute DEM is based on 30- by 30-meter data spacing with Universal Transverse Mercator(UTM) projection. Each 7.5- by 7.5-minute block provides the same coverage as the standard USGS 7.5-minute map series.
ScienceDex guides
Understand access before you commit
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.