Dataset Open Access
Hannah Kerner; Danika Wellington; Kiri Wagstaff; Jim Bell; Heni Ben Amor
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.5281/zenodo.1486196</identifier> <creators> <creator> <creatorName>Hannah Kerner</creatorName> <affiliation>Arizona State University</affiliation> </creator> <creator> <creatorName>Danika Wellington</creatorName> <affiliation>Arizona State University</affiliation> </creator> <creator> <creatorName>Kiri Wagstaff</creatorName> <affiliation>Jet Propulsion Laboratory</affiliation> </creator> <creator> <creatorName>Jim Bell</creatorName> <affiliation>Arizona State University</affiliation> </creator> <creator> <creatorName>Heni Ben Amor</creatorName> <affiliation>Arizona State University</affiliation> </creator> </creators> <titles> <title>Mars novelty detection Mastcam labeled dataset</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2018</publicationYear> <subjects> <subject>Mars, novelty detection, space, multispectral</subject> </subjects> <dates> <date dateType="Issued">2018-11-13</date> </dates> <resourceType resourceTypeGeneral="Dataset"/> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1486196</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1486195</relatedIdentifier> </relatedIdentifiers> <version>1</version> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>These datasets were used for experiments in the paper:<br> Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell III, J. F., Ben Amor, H. Novelty Detection for Multispectral Images with Application to Planetary Exploration.&nbsp;In Proceedings of&nbsp;<em>Innovative Applications in Artificial Intelligence (IAAI/AAAI)</em>, 2019.&nbsp;</p> <p>The file cae_train.zip contains examples used for training the convolutional autoencoder. Each example is a Numpy array (.npy) of size 64x64x6 pixels.</p> <p>The file novel_images.zip contains the 332 tiles labeled as &quot;novel&quot; for containing novel geologic features.&nbsp;Each example is a Numpy array (.npy) of size 64x64x6 pixels.</p> <p>The file cnn_direct.zip contains examples used for fine-tuning pre-trained networks. Images are divided into &quot;vis&quot; (shorter wavelengths) and &quot;nir&quot; (longer wavelengths) and by their label of &quot;typical&quot; vs. &quot;novel.&quot; These three-channel images are stored as .jpg files.</p> <p>All files are named with the following convention: sequence_id_XX*_{R,L}Y_solZZZZ_N.npy where XX* is the sequence ID for the image, {R,L}Y indicates the right (R) or left (L) eye of the camera and the image number in the sequence (Y), and ZZZZ is the four-digit sol (Martian day since the rover began operations) the image was acquired on.</p> <p>All source images are publicly released&nbsp;Experiment Data Records (EDRs) archived by&nbsp;the Planetary Data System (PDS).</p></description> </descriptions> </resource>
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