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34 results for “DTM”
Co-registered U. Arizona HiRISE DTM and ORI over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) of Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The DTM was originally processed by the University of Arizona (DTEEC_006855_1750_007501_1750_A01, https://www.uahirise.org/dtm/dtm.php?ID=PSP_006855_1750); this product is co-registered to CTX DTMs which were themselves co-registered to HRSC DTMs (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808357) using Ames Stereo Pipeline. The ORI was processed using Ames Stereo Pipeline and adjusted with GDAL.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 1 m/pixel<br> ORI resolution: 0.25 m/pixel</p> <p>Stereo pairs (from University of Arizona): PSP_006855_1750_RED, PSP_007501_1750_RED</p> <ul> </ul> <p>Image ID of the ORI: PSP_007501_1750_RED</p>
CTX DTM and ORI Mosaics over Sakarya Vallis, Gale Crater, Mars
<p>Local digital terrain model (DTM) and orthorectified image (ORI) mosaics over Sakarya Vallis, west of Aeolis Mons in Gale crater, Mars. The two constituent DTMs were processed using the CASP-GO suite described in Tao et al. (2018); the ORIs were processed using Ames Stereo Pipeline. The DTMs were then co-registered to an HRSC DTM mosaic (Persaud et al. 2021, https://doi.org/10.5281/zenodo.5808354) and each other using Ames Stereo Pipeline, and then cropped and mosaicked.</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> DTM grid-spacing: 18 m/pixel<br> ORI resolution: 6 m/pixel</p> <p>Stereo pairs (from Grindrod and Davis, 2018):</p> <ul> <li>P04_002675_1746_XI_05S222W, B21_017786_1746_XN_05S222W</li> <li>D02_027834_1748_XN_05S222W, G04_019698_1747_XI_05S222W</li> </ul> <p>Image IDs of the ORIs: P04_002675_1746_XI_05S222W, D02_027834_1748_XN_05S222W</p>
30-m HRSC DTM Mosaic of Gale Crater, Mars
<p>Digital terrain model (DTM) mosaic of Gale crater, Mars, processed from High-Resolution Stereo Camera (HRSC) stereo images using the modification of DLR-VICAR described by Kim and Muller (2009).</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> Grid-spacing: 30 m/pixel<br> Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018)</p> <p>HRSC source images: H1938_0000, H1927_0000, and H1916_0000</p>
Marzabotto, DTM della città etrusca.
<p>Il dataset contiene il modello digitale del terreno (DTM) della città antica di Kainua-Marzabotto. Il modello, di cui si forniscono sia il risultato finale che gli step intermedi, costituisce il punto di riferimento iniziale per il processo di ricostruzione della città etrusca di Marzabotto.</p>
Áramo - LiDAR 2023 (DTM 0.5m)
<h2>Abstract</h2> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>DTM 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>DTM 0.5m grid size, from LiDAR data 30/09+01/10/23</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>DTM</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Áramo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>AOI, Tile definition (SHP)</p> <p>Processing report (PDF)</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>15.12.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>DTM – based on average Z values of the ground points falling in the cell. </p> <p>Blocks 1x1 km</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.15m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>no updates planned</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4326</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Rolf Wilting/ Victoria Jadot</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Eurosense</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Rolf Wilting, rolfwilting@eurosense.com (until 01/24), Victoria Jadot, victoria.jadot@eurosense.com (from 02/24)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
UAE - MCC DTM
I have run MCC ground filter to get a DTM and this is the results. Where we have more dense houses, they were recognised as ground but didn't keep the shape of a house as seen in Fusion DTM. Source: Objaverse 1.0 / Sketchfab
NEANIAS UW-BAT Demo Data Shipwreck DTM
Digital terrain model (DTM) of shipwreck recorded with Teledyne T50-R echosounder. Format: GMT NetCDF
DTM, REM, and LiDAR of Five Sites in the St. Vrain Watershed
<p>Digital terrain models (drone and LiDAR) and the corresponding uav relative elevation model for Julia and Lindsay's project with The Watershed Center.</p>
Digital Terrain Model (DTM) from 2005 LiDAR for the Green Lakes Valley, Colorado
This 1m Digital Terrain Model (DTM) is derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain importan
Digital Terrain Model (DTM) shaded relief from 2005 LiDAR for the Green Lakes Valley, Colorado
This 1m Digital Terrain Model (DTM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DTM shaded relief was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DTM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data pr
Snow-Off Digital Terrain Model (DTM) shaded relief from 2010 LiDAR Niwot Ridge LTER Project Area, Colorado
Citation: Anderson, S.P., Qinghua, G., and Parrish, E.G., 2012, Snow-on and snow-off LiDAR point cloud data and digital elevation models for study of topography, snow, ecosystems, and environmental change at Boulder Creek Critical Zone Observatory, Colorado: Boulder Creek CZO, INSTAAR, University of Colorado at Boulder, digital media. This 1m Digital Terrain Model (DTM) shaded relief is a snow-off DTM derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from August 2010 for the Boulder Creek Critical Zone Observatory (CZO), near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from 1,375 LiDAR point cloud tiles subsampled from 10 points/m2 to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the Boulder Creek CZO and NCALM, both funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 598.92 km^2. The LiDAR point cloud data was acquired with an Optech Gemini Airborne Laser Terrain Mapper (ALTM) and mounted in a Piper Twin PA-31 Chieftain with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from four GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 0.5 x 1 km LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy is at least, but usually better than, 11 cm RMSE at 1 sigma and vertical accuracy is 5-30 cm RMSE at 1 sigma. The layer is available in IMAGINE format approx. 4 GB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum, with FGDC-compliant metadata. A shaded relief model was also generated. A similar la
Relocating the Full-Ocean-Depth Multifunctional Landers Seafloor Landing Point in the Challenger Deep Based On A Seismological Approach: A Precise Noninversive US-DTM Method Challenges the Traditional Inversive MC Method
<p>The SEGY format raw OBS-Lander data</p>
Comparison of DTM™ SCS Therapy Combined With Conventional Medical Management (CMM) to CMM Alone in the Treatment of Intractable Back Pain Subjects Without Previous History of Lumbar Spine Surgery
ClinicalTrials.gov study NCT06442410. IPD Sharing: NO. Countries: 4. Publications: 0.
DTM (TM) Spinal Cord Stimulation (SCS) Study
ClinicalTrials.gov study NCT04601454. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Snow-Off Digital Terrain Model (DTM) from 2010 LiDAR for the Boulder Creek Critical Zone Observatory (CZO), Colorado
This 1m Digital Terrain Model (DTM) is a snow-off DTM derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from August 2010 for the Boulder Creek Critical Zone Observatory (CZO), near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from 1,375 LiDAR point cloud tiles subsampled from 10 points/m2 to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the Boulder Creek CZO and NCALM, both funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 598.92 km^2. The LiDAR point cloud data was acquired with an Optech Gemini Airborne Laser Terrain Mapper (ALTM) and mounted in a Piper Twin PA-31 Chieftain with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from four GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 0.5 x 1 km LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy is at least, but usually better than, 11 cm RMSE at 1 sigma and vertical accuracy is 5-30 cm RMSE at 1 sigma. The layer is available in IMAGINE format approx. 4 GB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. Accessory layers consist of index map layers for point cloud tiles and flight lines, each with detailed attribute information such as acquisition date and tile file name. The DTM is available through an unrestricted public license. Other LiDAR DSMs, DTMs, and point cloud data ava
LiDAR and DTM Data from Tapajos National Forest in Para, Brazil, 2008
This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the Tapajos National Forest in Belterra municipality, Para, Brazil during late June and early July 2008. The surveys encompass the K67 and K83 eddy flux towers and a deforestation chronosequence managed through the Large-Scale Biosphere-Atmosphere Experiment in Amazonia providing long-term flux measurements of carbon dioxide. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.
LiDAR and DTM Data from Forested Land Near Manaus, Amazonas, Brazil, 2008
This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the K34 tower site in the Cuieiras Biological Reserve, over forest inventory plots in the Adolpho Ducke Forest Reserve, and over sites of the Biological Dynamics of Forest Fragments Project (BDFFP) in Rio Preto da Eva municipality near Manaus, Amazonas, Brazil during June 2008. The surveys encompass the K34 eddy flux tower managed through the Large-scale Biosphere-Atmosphere Experiment in Amazonia, forest inventory plots managed by the Programa de Pesquisa em Biodiversidade (PPBio), and sites managed by the BDFFP. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time.
UAE - Fusion DTM
I have run FUSION GroundFilter to get a DTM and this is the results. Where houses are denser he recognised them as ground. Source: Objaverse 1.0 / Sketchfab
Differential Target Multiplexed Spinal Cord Stimulation (DTM-SCS®) Real World Outcomes
ClinicalTrials.gov study NCT04725838. IPD Sharing: NO. Countries: 1. Publications: 0.
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