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paper/paper.bib

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@@ -117,4 +117,5 @@ @misc{rastervision
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url="https://github.com/azavea/raster-vision",
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author="Fishgold, Lewis and Hassan, Adeel and Emanuele, Rob and McClain, James and Kassel, Simon and Zhao, Annie and {jpolchlo} and Morrison, Joe and Bakker, Laurens and Santucci, James and Holeman, Nathan and {rbreslow} and {Taylor} and Park, Bborie and Liedman, Per and Kalra, Umang and Sani, Ammar and Meier, Andreas and Brown, Chris and McCallum, Chuck and Kettler, Cole and Cheipesh, Eugene and {Grigory} and Maleski, Jerome and Bertrand, Matt and Uryu, Shinya",
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doi="10.5281/zenodo.8018177",
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year=2023,
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}

paper/paper.jats

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@@ -136,8 +136,8 @@ a Creative Commons Attribution 4.0 International License (CC BY
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<p>Several unified libraries like torchgeo
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(<xref alt="Stewart et al., 2022" rid="ref-Stewart_TorchGeo_Deep_Learning_2022" ref-type="bibr">Stewart
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et al., 2022</xref>) and rastervision
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(<xref alt="Fishgold et al., n.d." rid="ref-rastervision" ref-type="bibr">Fishgold
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et al., n.d.</xref>) exists, but they are primarily targeted for
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(<xref alt="Fishgold et al., 2023" rid="ref-rastervision" ref-type="bibr">Fishgold
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et al., 2023</xref>) exists, but they are primarily targeted for
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PyTorch user community. Some efforts for GEE &amp; TensorFlow users,
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such as geemap
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(<xref alt="Wu, 2020" rid="ref-Wu2020" ref-type="bibr">Wu,
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<name><surname>Uryu</surname><given-names>Shinya</given-names></name>
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</person-group>
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<article-title>Raster Vision: An open source library and framework for deep learning on satellite and aerial imagery (2017-2023)</article-title>
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<year iso-8601-date="2023">2023</year>
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<uri>https://github.com/azavea/raster-vision</uri>
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<pub-id pub-id-type="doi">10.5281/zenodo.8018177</pub-id>
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</element-citation>

paper/paper.pdf

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