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4 results for “Curiosity rover”
Water and chlorine in the Martian subsurface along the traverse of NASA's Curiosity rover: DAN measurement profiles along the traverse
<p>This dataset contains a water map and a table of water and chlorine content in shallow Martian subsurface derived from the DAN instrument data from a landing site up to MSL sol 3333. DAN is a neutron spectrometer onboard the NASA’s Curiosity rover (see Mitrofanov, I. G., et al., (2012). Dynamic Albedo of Neutrons (DAN) experiment onboard NASA’s Mars Science Laboratory. Space Science Reviews, 170(1–4), 559–582. <a href="https://doi.org/10.1007/s11214-012-9924-y">https://doi.org/10.1007/s11214-012-9924-y</a>).</p> <p>The DAN instrument consists of two separate units: the DAN DE is the detector and electronics block, the DAN PNG is pulsed neutron generator. The DAN DE contains two proportional counters filled with <sup>3</sup>He gas for recording thermal and epithermal neutrons up to energy of 100 eV (CTN detector) and epithermal neutrons from 0.4 eV up to 100 eV (CETN detector). DAN provides two types of measurements: active and passive. When DAN DE operates in the passive mode, its detectors record the local neutron background. In the active mode the DAN PNG unit generates short pulses of 14 MeV neutrons, and DAN DE record additional counts of post-pulse emission of moderated neutrons after their interactions with nuclei of shallow subsurface.</p> <p>The results of active and passive DAN measurements along the traverse are assigned to two independent types of pixels: Pixel with Active Data (PAD) and Pixels of Passive Data (PPD). The content of water reported as Water Equivalent Hydrogen (WEH) for both kinds of pixel. The content of absorption equivalent chlorine (AEC) reported only for PAD. Statistical errors are given in each pixel. Method of active data analysis is described in Lisov, D. I., et al., (2018). Data Processing Results for the Active Neutron Measurements by the DAN Instrument on the Curiosity Mars Rover. Astronomy Letters, 44(7), 482–489. <a href="https://doi.org/10.1134/S1063773718070034">https://doi.org/10.1134/S1063773718070034</a>. The "Method of Referencing by Active Data" (MRAD) to analyze passive data was described in Nikiforov, S. Y., et al., (2020). Assessment of water content in Martian subsurface along the traverse of the Curiosity rover based on passive measurements of the DAN instrument. Icarus, 346, 113818. <a href="https://doi.org/10.1016/j.icarus.2020.113818">https://doi.org/10.1016/j.icarus.2020.113818</a>.</p> <p>PPD pixels are presented as squares in the water map, PAD are presented as circles. Table of water and chlorine contains seven values for each pixel: (1) is the successive number of pixel, (2) is a mark of its type, either PAD or PPD, (3) is longitude and (4) is latitude coordinates of the center of the pixel, (5) is the associated member of the MSL stratigraphic column, and (6) is estimated WEH values (wt.%) in PAD or PPD and (7) is estimated AEC value in PAD (wt.%).</p>
Mars surface image (Curiosity rover) labeled data set
<p>This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each.</p> <p>We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .</p> <p><strong>Contents</strong>:</p> <ul> <li>calibrated/: Directory containing calibrated MSL images</li> <li>train-calibrated-shuffled.txt: Training labels (images in shuffled order)</li> <li>val-calibrated-shuffled.txt: Validation labels</li> <li>test-calibrated-shuffled.txt: Test labels</li> <li>msl_synset_words-indexed.txt: Mapping from class IDs to class names</li> </ul> <p><strong>Attribution</strong>:</p> <p>If you use this data set in your own work, please cite this DOI:</p> <p>10.5281/zenodo.1049137</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>
MSL Curiosity Rover Images with Science and Engineering Classes
<p> </p> <p><strong>Please note that the file msl-labeled-data-set-v2.1.zip</strong><strong> below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p><strong>Data Set Description</strong></p> <p>The data set consists of 6,820 images that were collected by the Mars Science Laboratory (MSL) Curiosity Rover by three instruments: (1) the Mast Camera (Mastcam) Left Eye; (2) the Mast Camera Right Eye; (3) the Mars Hand Lens Imager (MAHLI). With the help from Dr. Raymond Francis, a member of the MSL operations team, we identified 19 classes with science and engineering interests (see the "Classes" section for more information), and each image is assigned with 1 class label. We split the data set into training, validation, and test sets in order to train and evaluate machine learning algorithms. The training set contains 5,920 images (including augmented images; see the "Image Augmentation" section for more information); the validation set contains 300 images; the test set contains 600 images. The training set images were randomly sampled from sol (Martian day) range 1 - 948; validation set images were randomly sampled from sol range 949 - 1920; test set images were randomly sampled from sol range 1921 - 2224. All images are resized to 227 x 227 pixels without preserving the original height/width aspect ratio.</p> <p><strong>Directory Contents</strong></p> <ul> <li>images - contains all 6,820 images</li> <li>class_map.csv - string-integer class mappings</li> <li>train-set-v2.1.txt - label file for the training set</li> <li>val-set-v2.1.txt - label file for the validation set</li> <li>test-set-v2.1.txt - label file for the test set</li> </ul> <p>The label files are formatted as below:</p> <p>"Image-file-name class_in_integer_representation"</p> <p><strong>Labeling Process</strong></p> <p>Each image was labeled with help from three different volunteers (see Contributor list). The final labels are determined using the following processes:</p> <ul> <li>If all three labels agree with each other, then use the label as the final label.</li> <li>If the three labels do not agree with each other, then we manually review the labels and decide the final label.</li> <li>We also performed error analysis to correct labels as a post-processing step in order to remove noisy/incorrect labels in the data set. </li> </ul> <p><strong>Classes</strong></p> <p>There are 19 classes identified in this data set. In order to simplify our training and evaluation algorithms, we mapped the class names from string to integer representations. The names of classes, string-integer mappings, distributions are shown below:</p> <p>Class name, counts (training set), counts (validation set), counts (test set), integer representation</p> <p>Arm cover, 10, 1, 4, 0</p> <p>Other rover part, 190, 11, 10, 1</p> <p>Artifact, 680, 62, 132, 2</p> <p>Nearby surface, 1554, 74, 187, 3</p> <p>Close-up rock, 1422, 50, 84, 4</p> <p>DRT, 8, 4, 6, 5</p> <p>DRT spot, 214, 1, 7, 6</p> <p>Distant landscape, 342, 14, 34, 7</p> <p>Drill hole, 252, 5, 12, 8</p> <p>Night sky, 40, 3, 4, 9</p> <p>Float, 190, 5, 1, 10</p> <p>Layers, 182, 21, 17, 11</p> <p>Light-toned veins, 42, 4, 27, 12</p> <p>Mastcam cal target, 122, 12, 29, 13</p> <p>Sand, 228, 19, 16, 14</p> <p>Sun, 182, 5, 19, 15</p> <p>Wheel, 212, 5, 5, 16</p> <p>Wheel joint, 62, 1, 5, 17</p> <p>Wheel tracks, 26, 3, 1, 18</p> <p> </p> <p><strong>Image Augmentation</strong></p> <p>Only the training set contains augmented images. 3,920 of the 5,920 images in the training set are augmented versions of the remaining 2000 original training images. Images taken by different instruments were augmented differently. As shown below, we employed 5 different methods to augment images. Images taken by the Mastcam left and right eye cameras were augmented using a horizontal flipping method, and images taken by the MAHLI camera were augmented using all 5 methods. Note that one can filter based on the file names listed in the train-set.txt file to obtain a set of non-augmented images.</p> <ul> <li>90 degrees clockwise rotation (file name ends with -r90.jpg)</li> <li>180 degrees clockwise rotation (file name ends with -r180.jpg)</li> <li>270 degrees clockwise rotation (file name ends with -r270.jpg)</li> <li>Horizontal flip (file name ends with -fh.jpg)</li> <li>Vertical flip (file name ends with -fv.jpg)</li> </ul> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank the volunteers (as in the Contributor list) who provided annotations for this data set. We would also like to thank the PDS Imaging Note for the continuous support of this work.</p>
Mars surface image (Curiosity rover) labeled data set version 1
This data set consists of 6691 images spanning 24 classes that were collected by the Mars Science Laboratory (MSL, Curosity) rover by three instruments (Mastcam Right eye, Mastcam Left eye, and MAHLI). These images are the "browse" version of each original data product, not full resolution. They are roughly 256x256 pixels each. We divided the MSL images into train, validation, and test data sets according to their sol (Martian day) of acquisition. This strategy was chosen to model how the system will be used operationally with an image archive that grows over time. The images were collected from sols 3 to 1060 (August 2012 to July 2015). The exact train/validation/test splits are given in individual files. Full-size images can be obtained from the PDS at https://pds-imaging.jpl.nasa.gov/search/ .
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