Journal article Open Access

DeepMoon Supplemental Materials

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


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    <dct:title>DeepMoon Supplemental Materials</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2018</dct:issued>
    <dcat:keyword>machine learning</dcat:keyword>
    <dcat:keyword>neural network</dcat:keyword>
    <dcat:keyword>convolutional neural network</dcat:keyword>
    <dcat:keyword>astronomy</dcat:keyword>
    <dcat:keyword>craters</dcat:keyword>
    <dcat:keyword>moon</dcat:keyword>
    <dcat:keyword>computer vision</dcat:keyword>
    <dcat:keyword>deep learning</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2018-01-31</dct:issued>
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    <dct:description>&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;</dct:description>
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