Journal article Open Access

DeepMoon Supplemental Materials

Silburt, Ari; Ali-Dib, Mohamad; Zhu, Chenchong; Jackson, Alan; Menou, Kristen


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  <identifier identifierType="DOI">10.5281/zenodo.1133969</identifier>
  <creators>
    <creator>
      <creatorName>Silburt, Ari</creatorName>
      <givenName>Ari</givenName>
      <familyName>Silburt</familyName>
      <affiliation>Centre for Planetary Sciences, University of Toronto at Scarborough; Department of Astronomy &amp; Astrophysics, University of Toronto; Department of Astronomy &amp; Astrophysics, Penn State University</affiliation>
    </creator>
    <creator>
      <creatorName>Ali-Dib, Mohamad</creatorName>
      <givenName>Mohamad</givenName>
      <familyName>Ali-Dib</familyName>
      <affiliation>Centre for Planetary Sciences, University of Toronto at Scarborough; Canadian Institute for Theoretical Astrophysics, University of Toronto</affiliation>
    </creator>
    <creator>
      <creatorName>Zhu, Chenchong</creatorName>
      <givenName>Chenchong</givenName>
      <familyName>Zhu</familyName>
      <affiliation>Department of Astronomy &amp; Astrophysics, University of Toronto; Canadian Institute for Theoretical Astrophysics, University of Toronto</affiliation>
    </creator>
    <creator>
      <creatorName>Jackson, Alan</creatorName>
      <givenName>Alan</givenName>
      <familyName>Jackson</familyName>
      <affiliation>Centre for Planetary Sciences, University of Toronto at Scarborough; Department of Astronomy &amp; Astrophysics, University of Toronto; School of Earth and Space Exploration, Arizona State University</affiliation>
    </creator>
    <creator>
      <creatorName>Menou, Kristen</creatorName>
      <givenName>Kristen</givenName>
      <familyName>Menou</familyName>
      <affiliation>Centre for Planetary Sciences, University of Toronto at Scarborough; Canadian Institute for Theoretical Astrophysics, University of Toronto</affiliation>
    </creator>
  </creators>
  <titles>
    <title>DeepMoon Supplemental Materials</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>machine learning</subject>
    <subject>neural network</subject>
    <subject>convolutional neural network</subject>
    <subject>astronomy</subject>
    <subject>craters</subject>
    <subject>moon</subject>
    <subject>computer vision</subject>
    <subject>deep learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-01-31</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1133969</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1133968</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/astronomy-general</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This database contains supplemental materials to the journal article, &amp;quot;Lunar Crater Identification via Deep Learning&amp;quot;, which trained a&amp;nbsp;convolutional neural network (convnet) to automate the identification of&amp;nbsp;lunar craters.&amp;nbsp; The database is meant to enable users&amp;nbsp;to reproduce our work, or apply our model and data&amp;nbsp;to new applications.&amp;nbsp; For more information about the project&amp;nbsp;please see the associated journal article.&amp;nbsp; For the code, DeepMoon, and instructions on how to use the files in this database, see the GitHub link below. For a brief description of each file in this database, see the file list below.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Code&lt;br&gt;
--------&lt;br&gt;
DeepMoon is available at https://github.com/silburt/DeepMoon&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
Files&lt;br&gt;
-------&lt;/p&gt;

&lt;p&gt;dev_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the validation dataset.&lt;/p&gt;

&lt;p&gt;dev_images.hdf5 - Input DEM images and output targets of the validation dataset.&amp;nbsp; Also includes each image&amp;#39;s longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.&lt;/p&gt;

&lt;p&gt;LunarLROLrocKaguya_118mperpix.png - LRO LOLA and Kaguya Terrain Camera DEM Merge, downsampled to 118 m/pixel and 8 bits/pixel.&amp;nbsp; The original file can be found at: https://astrogeology.usgs.gov/search/map/Moon/LRO/LOLA/Lunar_LRO_LrocKaguya_DEMmerge_60N60S_512ppd.&lt;/p&gt;

&lt;p&gt;model_keras1.2.2.h5 -&amp;nbsp;Keras model weights for the DeepMoon CNN, compatible with Keras version 1.2.2.&lt;/p&gt;

&lt;p&gt;model_keras2.h5 - Keras model weights for the DeepMoon CNN, compatible with Keras versions &amp;gt;= 2.0.&lt;/p&gt;

&lt;p&gt;post-processed_sample_images.zip - Contains a set of sample images from the test dataset with the Moon DEM image, new identified craters, CNN target predictions, and ground-truth. The new craters from these images were used to estimate the post-processed false positive rate. See Instructions.txt in .zip file for more details.&lt;/p&gt;

&lt;p&gt;post-processed_test_craters.npy - numpy file containing post-processed craters&amp;nbsp;identified by our pipeline&amp;nbsp;on the test set. Each crater entry is&amp;nbsp;arranged as a tuple: (longitude, latitude,&amp;nbsp;radii), where longitude and latitude are in degrees, and radius is in kilometres.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;test_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the test dataset.&lt;/p&gt;

&lt;p&gt;test_images.hdf5 - Input DEM images and output targets of the test dataset.&amp;nbsp; Also includes each image&amp;#39;s longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.&lt;/p&gt;

&lt;p&gt;train_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the training dataset.&lt;/p&gt;

&lt;p&gt;train_images.hdf5 - Input DEM images and output targets of the training dataset.&amp;nbsp; Also includes each image&amp;#39;s longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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