Journal article Open Access

DeepMoon Supplemental Materials

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

This database contains supplemental materials to the journal article, "Lunar Crater Identification via Deep Learning", which trained a convolutional neural network (convnet) to automate the identification of lunar craters.  The database is meant to enable users to reproduce our work, or apply our model and data to new applications.  For more information about the project please see the associated journal article.  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. 

Code
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DeepMoon is available at https://github.com/silburt/DeepMoon


Files
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dev_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the validation dataset.

dev_images.hdf5 - Input DEM images and output targets of the validation dataset.  Also includes each image's longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.

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

model_keras1.2.2.h5 - Keras model weights for the DeepMoon CNN, compatible with Keras version 1.2.2.

model_keras2.h5 - Keras model weights for the DeepMoon CNN, compatible with Keras versions >= 2.0.

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.

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

test_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the test dataset.

test_images.hdf5 - Input DEM images and output targets of the test dataset.  Also includes each image's longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.

train_craters.hdf5 - Pandas HDFStore of crater locations and sizes for images in the training dataset.

train_images.hdf5 - Input DEM images and output targets of the training dataset.  Also includes each image's longitude/latitude bounds, and the pixel bounds of the global DEM regions cropped to make each image.

 

Files (30.9 GB)
Name Size
dev_craters.hdf5
md5:d569dd772b9185800347cd1ace7ee0c4
181.4 MB Download
dev_images.hdf5
md5:dbd05ba31eac639c3e6f4efaaa946ae9
9.9 GB Download
LunarLROLrocKaguya_118mperpix.png
md5:b45391fd1d497814a05fdade7419fb85
274.4 MB Download
model_keras1.2.2.h5
md5:58f74664ea6f9cb87dcf88d71149946d
123.4 MB Download
model_keras2.h5
md5:f9d8780b9b7bac383125e60d759b66cc
123.4 MB Download
post-processed_sample_images.zip
md5:e14bed2aab884b941cdf1e68663448de
33.7 MB Download
post-processed_test_craters.npy
md5:62df1d8de3cf4c5d105b997b012dc3e0
375.9 kB Download
test_craters.hdf5
md5:f5bf4de056b1151e76c8239532dca350
264.8 MB Download
test_images.hdf5
md5:9045f7f91a0a10175a6e384a8bbabf63
9.9 GB Download
train_craters.hdf5
md5:e8acede7db01409b1bec51cea7816e03
261.7 MB Download
train_images.hdf5
md5:83c1f8b4c7c0c9a9a9c7674ff489ef0a
9.9 GB Download
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