CraterNet

A Fully Convolutional Neural Network for Lunar Crater Detection Based on Remotely Sensed Data

Here you have access to the full pdf describing the project developed in my Master Thesis : http://goo.gl/4eMhyv

You also have access to the training data used via the CSV file containing all required information to download the crater patches to train a supervised model designed to recognize craters. Also, the Annotations folder contains all raw annotations with associated label.

The NAC CDR images of Lunar Scenes can be downloaded free-of-charge on the Lunar Orbital Data Explorer (https://goo.gl/7j8CjM) and the ortho-rectified images can be found on this link : https://goo.gl/qivZYt

Data can be acessed using the command :

svn checkout https://github.com/QuentinGlaude/CraterNet/trunk/Annotations/

If you make use of these data, please reference me as :

• Glaude, Q. (2017). CraterNet : a Fully Convolutional Neural Network for Lunar Crater Detection Based on Remotely Sensed Data. Master's thesis. University of Liege. Liege, Belgium.