Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic
  Segmentation   To address the mounting destruction caused by floods in climate-vulnerable
regions, we propose Street to Cloud, a machine learning pipeline for
incorporating crowdsourced ground truth data into the segmentation of satellite
imagery of floods. We propose this approach as a solution to the
labor-intensive task of generating high-quality, hand-labeled training data,
and demonstrate successes and failures of different plausible crowdsourcing
approaches in our model. Street to Cloud leverages community reporting and
machine learning to generate novel, near-real time insights into the extent of
floods to be used for emergency response.

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The categories of this paper are: math
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The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat