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9 results for “front mapping”
Front polylines extracted from DWD Maps
<p>Weather fronts extracted from surface analysis maps of the German Weather Service (Deutscher Wetterdienst, DWD) for the years 2015 to 2020</p> <p>Fronts are described by an identifier (warm, cold, occ) followed by several [lat, lon] coordinate pairs in degree [North, East]</p> <p>Stationary-Fronts are extracted as either cold or warm fronts</p> <p>e.g.</p> <p>warm [75.69211706913667, 29.558180185836815] [75.12348547753966, 26.89164117647018] ...</p> <p>No guarantee is given regarding correctness or completeness of extracted fronts</p>
Figure 5. A front view of the "Mary and John Gray Library"-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 5 represents the avatar standing outside and in front of the Mary and John Gray Library after selecting the option “Library”. The library’s main purpose is to facilitate students with a variety of scholarly information within the overall composition of the University’s stated mission. Figure 5 shows the path generated by A* algorithms with a red color.</p>
Fracture maps and calving fronts for Thwaites Glacier western terminus 2015-2021
<p>These data comprise observations of severe crevassing and calving front position over the Thwaites Glacier Ice Tongue (TGIT) between 2015 and 2021 in geotiff form, along with bitmap versions of Sentinel-1 backscatter images from which the observations were derived. A version of UNet was used to create the data from the backscatter images.<br> These data were collected in 2021 for the study of structural change on the TGIT.</p> <p>File information: tgit_cfs.tar.gz is a gz-compressed directory of binary calving front segmentations of the Thwaites Glacier Ice Tongue in geotiff format.<br> tgit_fms.tar.gz is a gz-compressed directory of binary fracture segmentations of the Thwaites Glacier Ice Tongue in geotiff format.</p>
Mapping of the calving front of Eqip Sermia Glacier, West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2018 at Eqip Sermia Glacier. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>For each, processed and raw, data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM or GLACIER_YYYYMMDD_HHMM_N.</p> <p>For instance : eqip_20180708_1225 contains the Data of the LARGE-SCALE survey of Eqip glacier that started on July 8, 2018 at 12:25 UTC time. eqip_20180707_1245_1 contains the Data of the FIRST repeat survey of the calving front of Eqip glacier that started on July 7, 2018 at 12:45 UTC time, eqip_20180707_1245_2 is the second repeat survey, eqip_20180707_1245_3 is the third and eqip_20180707_1245_4 is the last.</p> <p>Coordinate system used is UTM Zone 22W based on WGS84</p> <p>Take-off and landing site latitude and longitude was ( 69.757767 , - 50.228172 )</p> <p>This dataset is related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. V. Ryan, Frontiers in Earth Sciences.</p> <p> </p> <p> </p>
Mapping of the calving front of Hart, Sharp, Melville and Farquhar glaciers, Northwest Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : farquhar_20170705_1914 contains the Data of the survey of Farquhar glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>This paper is related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. V. Ryan, Frontiers in Earth Sciences</p> <p> </p>
Mapping of the calving front of Heilprin Glacier, North West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : heilprin_20170705_1914 contains the Data of the survey of Heilprin glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>The data are related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. Ryan, Frontiers in Earth Sciences</p>
Mapping of the calving front of Tracy Glacier, North West Greenland, by UAV photogrammetry
<p>These data contain photogrammetrical data collected by unmanned aerial vehicle (UAV) in July 2017 in the Inglefield Bredning. The data include processed data (orthoimage and digital elevation models) with the Structure-fom-Motion photogrammetrical software Agisoft Photoscan.</p> <p>Data are organized by glacier surveys with the following convention GLACIER_YYYYMMDD_HHMM. For instance : tracy_20170705_1914 contains the Data of the survey of Tracy glacier that started on July 5, 2017 at 19:14 UTC time.</p> <p>Coordinate system used is UTM Zone 19N based on WGS84</p> <p>Take-off and landing site lattitude and longitude was ( 77.497424 , -66.678433 )</p> <p>The data are related to the article "High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland", G. Jouvet, Y. Weidmann, E. van Dongen, M. Lüthi, A. Vieli, J. Ryan, Frontiers in Earth Sciences</p>
Data and Code for manuscript titled "Global mapping and evolution of persistent fronts in Large Marine Ecosystems over the past 40 years"
<h1>A dataset of global persistent fronts around Large Marine Ecosystems.</h1> <h2>Xing, Q., Yu, H. & Wang, H. Global mapping and evolution of persistent fronts in Large Marine Ecosystems over the past 40 years. Nat Commun 15, 4090 (2024). https://doi.org/10.1038/s41467-024-48566-w</h2>
DL-FRONT MERRA-2 weather front probability maps over North America, 1980-
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) <a href="https://www.wpc.ncep.noaa.gov/html/sfc2.shtml">Coded Surface Bulletin</a> dataset. The training datasets covered the years 2003-2007.</p> <p>The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.</p> <p>The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.</p>
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