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Using data-driven algorithms for semi-automated geomorphological mapping.

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Summary

Direct Sampling (DS) and Random Forest (RF) algorithms offer accurate, semi-automated geomorphological mapping in alpine regions. DS provides a less noisy spatial distribution compared to RF.

Keywords:
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Area of Science:

  • Geosciences
  • Environmental Science
  • Computer Science

Background:

  • Geomorphological mapping is crucial for understanding alpine environments.
  • Manual mapping is time-consuming and labor-intensive.
  • Data-driven algorithms offer potential for semi-automated classification.

Purpose of the Study:

  • To compare the performance of Direct Sampling (DS) and Random Forest (RF) algorithms for semi-automated geomorphological mapping in alpine environments.
  • To evaluate the suitability of DS for geomorphological classification, an area not previously investigated.
  • To assess the accuracy and spatial distribution of classes generated by both algorithms against field-validated ground truth.

Main Methods:

  • Two data-driven algorithms, Direct Sampling (DS) and Random Forest (RF), were employed.
  • A manually mapped zone served as the training dataset.
  • Predictor variables were used to classify a target zone, with results validated against a field-geomorphological map.

Main Results:

  • Both DS and RF algorithms achieved satisfactory performance with similar accuracy and Cohen's Kappa values.
  • Direct Sampling (DS) produced a less spatially noisy map compared to Random Forest (RF).
  • DS's advantage is attributed to its consideration of spatial class dependence.

Conclusions:

  • Direct Sampling (DS) and Random Forest (RF) are both suitable for semi-automated geomorphological mapping in alpine regions at a regional scale.
  • DS demonstrates a potential advantage in preserving spatial relationships between geomorphological classes.
  • The study opens avenues for further advancements in automated geomorphological analysis.