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On Training Set Selection in Spatial Deep Learning

In: High-Dimensional Optimization and Probability

Author

Listed:
  • Eligius M. T. Hendrix

    (Universidad de Málaga)

  • Mercedes Paoletti

    (Universidad de Extremadura)

  • Juan Mario Haut

    (Universidad de Extremadura)

Abstract

The careful design of experiments in spatial statistics aims at estimating models in an accurate way. In the field of spatial deep learning to classify spatial observations, the training set used to calibrate a model or network is usually determined in a random way in order to obtain a representative sample. This chapter will sketch with examples that this is not necessarily the best way to proceed. Moreover, as in some cases windows are used to smooth signals, overlap may occur in the spatial data. On the one hand, this implies auto-correlation in the training set and, on the other hand, a correlation among pixels used for training and for testing. Our question is how to measure such an overlap and how to steer the selection of training sets. We describe an optimization problem to model and minimize the auto-correlation. A simple example is used to capture the concepts of design of experiments versus training set selection and the measurement of the overlap.

Suggested Citation

  • Eligius M. T. Hendrix & Mercedes Paoletti & Juan Mario Haut, 2022. "On Training Set Selection in Spatial Deep Learning," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 327-339, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-00832-0_9
    DOI: 10.1007/978-3-031-00832-0_9
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    Cited by:

    1. Malmquist, Louise & Barron, Jennie, 2023. "Improving spatial resolution in soil and drainage data to combine natural and anthropogenic water functions at catchment scale in agricultural landscapes," Agricultural Water Management, Elsevier, vol. 283(C).

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