Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seamless Satellite-image Synthesis   We introduce Seamless Satellite-image Synthesis (SSS), a novel neural
architecture to create scale-and-space continuous satellite textures from
cartographic data. While 2D map data is cheap and easily synthesized, accurate
satellite imagery is expensive and often unavailable or out of date. Our
approach generates seamless textures over arbitrarily large spatial extents
which are consistent through scale-space. To overcome tile size limitations in
image-to-image translation approaches, SSS learns to remove seams between tiled
images in a semantically meaningful manner. Scale-space continuity is achieved
by a hierarchy of networks conditioned on style and cartographic data. Our
qualitative and quantitative evaluations show that our system improves over the
state-of-the-art in several key areas. We show applications to texturing
procedurally generation maps and interactive satellite image manipulation.

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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol