Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SatImNet: Structured and Harmonised Training Data for Enhanced Satellite
  Imagery Classification   Automatic supervised classification with complex modelling such as deep
neural networks requires the availability of representative training data sets.
While there exists a plethora of data sets that can be used for this purpose,
they are usually very heterogeneous and not interoperable. In this context, the
present work has a twofold objective: i) to describe procedures of open-source
training data management, integration, and data retrieval, and ii) to
demonstrate the practical use of varying source training data for remote
sensing image classification. For the former, we propose SatImNet, a collection
of open training data, structured and harmonized according to specific rules.
For the latter, two modelling approaches based on convolutional neural networks
have been designed and configured to deal with satellite image classification
and segmentation.

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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin