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37 results for “HiRISE”
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>
HiRISE DTMs generated using NASA's Ames Stereo Pipeline
<p>In an effort to better understand the surface roughness of Martian lava flows, we generated over 30 HiRISE DTMs using ISIS3 and ASP and extracted their roughness (Rodriguez Sanchez-Vahamonde and Neish, 2020). We have posted these DTMs for public use here. </p> <p>HiRISE stereo images typically have a spatial sampling of 25 - 50 centimeters, providing us with DTMs of 1 - 2 meters per pixel. We also converted the HiRISE stereo-pair ID for each product into its proper DTM ID using the NASA Planetary Data System product naming convention for HiRISE DTMs (<a href="https://www.uahirise.org/dtm/about.php">https://www.uahirise.org/dtm/about.php</a>; last accessed 18.09.2019).</p>
Mars orbital image (HiRISE) labeled data set
<p>This data set contains 3820 landmarks that were extracted from 168 HiRISE images. The landmarks were detected in HiRISE browse images. For each landmark, we cropped a square bounding box the included the full extent of the landmark plus a 30-pixel margin to left, right, top, and bottom. Each cropped image was then resized to 227x227 pixels.</p> <p><strong>Contents</strong>:</p> <ul> <li>map-proj/: Directory containing individual cropped landmark images</li> <li>labels-map-proj.txt: Class labels (ids) for each landmark image</li> <li>landmark_mp.py: Python dictionary that maps class ids to semantic names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI: 10.5281/zenodo.1048301</p> <p>Please also cite this paper, which provides additional details about the data set.</p> <p>Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, and Jordan Padams. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." <em>Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence</em>, 2018.</p> <p> </p>
HiRISE Image Patches Obscured by Atmospheric Dust
<p><strong>Overview</strong></p> <p>The purpose of this dataset is to train a classifier to detect "dusty" versus "not dusty" patches within browse-resolution HiRISE observations of the Martian surface. Here, "dusty" refers to images in which the view of the surface has been obscured heavily by atmospheric dust.</p> <p>The dataset contains two sets of 20,000 image patches each from EDR (full resolution) and RDR ("browse" resolution) non-map-projected ("nomap") HiRISE images, with balanced classes. The patches have been split into train (n = 10,000), validation (n = 5,000), and test (n = 5,000) sets such that no two patches from the same HiRISE observation appear in more than one of these subsets. There could be some noise in the labels, but a subset of the validation images have been manually vetted so that label noise rates can be estimated. More details on the dataset creation process are described below.</p> <p> </p> <p><strong>Generating Candidate Images and Patches</strong></p> <p>To begin constructing the dataset, the paper "The origin, evolution, and trajectory of large dust storms on Mars during Mars years 24–30 (1999–2011)," by Wang and Richardson (2015), was used to compile a set of time ranges for which global or regional dust storms were known to be occurring on Mars. All HiRISE RDR nomap browse images acquired within these time ranges were then inspected manually to determine sets of images that were (1) almost entirely obscured by dust and (2) almost entirely clear of dust. Then, 10,000 patches from the two subsets of images were extracted to form the "dusty" and "not dusty" classes. The extracted patches are 100-by-100 pixels, which roughly corresponds to the width of one CCD channel within the browse image (the width of the raw EDR data products that are stitched together to form a full RDR image). Some small amount of label noise is introduced in this process, since a patch from a mostly dusty image might happen to contain a clear view of the ground, and a patch from a mostly non-dusty image might contain some dust or regions on the surface that are featureless and appear like dusty patches. A set of "vetting labels" is included, which includes human annotations by the author for a subset of the validation set of patches. These labels can be used to estimate the apparent label noise in the dataset.</p> <p>Corresponding to the RDR patch dataset, a set of patches are extracted from the same set of EDR images for the "dusty" and "not dusty" classes. EDRs are raw images from the instrument that have not been calibrated or stitched together. To provide some form of normalization, EDR patches are only extracted from the lower half of the EDRs, with the upper half being used to perform a basic calibration of the lower half. Basic calibration is done by subtracting the sample (image column) averages from the upper half to remove "striping," then computing the 0.1<sup>th</sup> and 99.9<sup>th</sup> percentiles of the remaining values in the upper half and stretching the image patch to 8-bit integer values [0, 255] within that range. The calibration is meant to implement a process that could be performed onboard the spacecraft as the data is being observed (hence, using the top half of the image acquired first to calibrate the lower half of the image which is acquired later). The full resolution EDRs, which are 1024 pixels wide, are resized down to 100-by-100 pixel patches after being extracted so that they roughly match the resolution of the patches from the RDR browse images.</p> <p> </p> <p><strong>Archive Contents</strong></p> <p>The compressed archive file contains two top-level directories with similar contents, "edr_nomap_full_resized" and "rdr_nomap_browse." The first directory contains the dataset constructed from EDR data and the second contains the dataset constructed from RDR data.</p> <p>Within each directory, there are "dusty" and "not_dusty" directories containing the image patches from each class, "manifest.csv," and "vetting_labels.csv." The vetting labels file contains a list of manually labeled examples, along with the original labels to make it easier to compute label noise rates. The "manifest.csv" file contains a list of every example, its label, and whether it belongs to the train, validation, or test set.</p> <p>An example ID encodes information about where the patch was sampled from the original HiRISE image. As an example from the RDR dataset, the ID "003100_PSP_004440_2125_r4805_c512" can be broken into several parts:</p> <ul> <li>"003100" is a unique numerical ID</li> <li>"PSP_004440_2125" is the HiRISE observation ID</li> <li>"r4805_c512" means the patch's upper left corner starts at the 4805<sup>th</sup> row and 512<sup>th</sup> column of the original observation</li> </ul> <p>For the EDR dataset, the ID "200000_PSP_004530_1030_RED7_1_r9153" is broken down as follows:</p> <ul> <li>"200000" is a unique numerical ID</li> <li>"PSP_004530_1030" is the HiRISE observation ID</li> <li>"RED7" is the CCD ID</li> <li>"1" is the CCD channel (either 0 or 1)</li> <li>"r9153" means that the patch is extracted starting at the 9153<sup>rd</sup> row (since all columns of the 1024-pixel EDR are used, no column is specified; it is implicitly always 0)</li> </ul> <p><strong>Original Data</strong></p> <p>The original HiRISE EDR and RDR data is available via the Planetary Data System (PDS), hosted at <a href="https://hirise-pds.lpl.arizona.edu/PDS/">https://hirise-pds.lpl.arizona.edu/PDS/</a></p>
023488_2050_zoom_location_004_mars_hirise
023488_2050_zoom_location_004_mars_hirise depression -- another look into this area: https://skfb.ly/6MW87 more info may be available here: "Proposed Landing Site in Mawrth Vallis" https://www.uahirise.org/dtm/dtm.php?ID=ESP_023488_2050 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
008437_1750_overview_mars_hirise
008437_1750_overview_mars_hirise mapping the area... more info may be available here: "Layered Outcrop in Gale Crater" https://www.uahirise.org/dtm/dtm.php?ID=PSP_008437_1750 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
002437_1875_overview_mars_hirise
002437_1875_overview_mars_hirise butter smooth curves ahead... more info may be available here: "Landslides Along Shoreline in Elysium Planitia" https://hirise.lpl.arizona.edu/dtm/dtm.php?ID=PSP_002437_1875 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
051986_1750_overview_mars_hirise
051986_1750_overview_mars_hirise escarpo ala ceti ... more info may be available here: "Scarp on Ceti Mensa Layered Deposit" https://www.uahirise.org/dtm/dtm.php?ID=ESP_051986_1750 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
017399_1680_overview_mars_hirise
017399_1680_overview_mars_hirise floor of a crater...and..more dunes :) more info may be available here: "Light-Toned Layering in Noctis Region Pit" https://www.uahirise.org/dtm/dtm.php?ID=ESP_017399_1680 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
019231_1905_overview_mars_hirise
019231_1905_overview_mars_hirise more slopes present in this overview dataset more info may be available here: "Lobate Flow Front in Kasei Valles" https://www.uahirise.org/dtm/dtm.php?ID=ESP_019231_1905 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
049897_1980_overview_mars_hirise
049897_1980_overview_mars_hirise here's an overview mesh for another location imaged by NASA as a possible landing site more info may be available here: "Candidate Landing Site for 2020 Mission in Northeast Syrtis Major Region" https://hirise.lpl.arizona.edu/dtm/dtm.php?ID=ESP_049897_1980 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
025625_1580_overview_mars_hirise
025625_1580_overview_mars_hirise "Dunes on Ridges, eh? --- cheeky" -R.G. more info may be available here: "Dunes on Ridges" https://www.uahirise.org/dtm/dtm.php?ID=ESP_025625_1580 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
032864_1595_overview_mars_hirise
032864_1595_overview_mars_hirise another overview mesh for another candidate area on our desert neighbor... more info may be available here: "Light-Toned Layered Deposits at Contact between Ladon Valles and Basin" https://www.uahirise.org/dtm/dtm.php?ID=ESP_032864_1595 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
034419_2015_overview_mars_hirise
034419_2015_overview_mars_hirise Indeed, the sand dune and rocky region interface is what brought us here in the first place ... more info may be available here: "Becquerel Crater Dune and Yardang Interactions" https://www.uahirise.org/dtm/dtm.php?ID=ESP_034419_2015 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
020673_1750_overview_mars_hirise
020673_1750_overview_mars_hirise Scroll-Bars, eh? ... let's have a glance .. more info may be available here: "Martian Meanders and Scroll-Bars" https://www.uahirise.org/dtm/dtm.php?ID=ESP_020673_1750 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
023488_2050_overview_mars_hirise
023488_2050_overview_mars_hirise "proposed landing sight" ;) more info may be available here: "Proposed Landing Site in Mawrth Vallis" https://www.uahirise.org/dtm/dtm.php?ID=ESP_023488_2050 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
036481_1835_overview_mars_hirise
036481_1835_overview_mars_hirise on what scale are we measuring "new" here ;) more info may be available here: "New Impact Site" https://www.uahirise.org/dtm/dtm.php?ID=ESP_036481_1835 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
HiRISE and MOC-NA Images Used in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These files contain the images used in the paper "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"</p> <p>All images examined, and all images used in the creation of figures are listed within these documents. They are seperated by region, and type of image (HiRISE, MOC-NA)</p>
Martian Frost in HiRISE Observations of Northern Mid-Latitude Craters
<p>This dataset contains a labeled set of High Resolution Imaging Science Experiment (HiRISE) image tiles that either do or do not contain visible indications of frost presence. The dataset was created using Labelbox to draw polygonal annotations over subframes extracted from HiRISE observations. Then, each subframe was broken into 299x299 pixel tiles at 0.5 m/pixel resolution, and a majority vote across annotations is used to determine whether each tile is given a "frost," "background," or "ambiguous" label. Ambiguous labels are assigned if there is not a majority agreement about the label of the tile given the polygons. All tiles from "background" subframes (taken during summer months when no frost is present) are assigned a "background" label. Corresponding to each tile is a JSON label file containing metadata from overlapping annotations. There is also a TensorFlow TFRecord object saved for each subframe that contains the set of all tiles extracted from that subrame.</p>
020946_1450_zoom_location_001_mars_hirise
020946_1450_zoom_location_001_mars_hirise the "Knob" as referred to here: https://skfb.ly/6MVKG more info may be available here: "Light-Toned Rock" https://www.uahirise.org/dtm/dtm.php?ID=ESP_020946_1450 This signal was analyzed by Organic. Tools used: gdal, qgis, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
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OpenNeuro
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