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geodl: An R package for geospatial deep learning semantic segmentation using torch and terra

  • 0Department of Geology and Geography, West Virginia University, Morgantown, WV, United States of America.
Plos One +

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December 5, 2024

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December 5, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

The new geodl R package enables deep learning (DL) for geospatial data analysis in R, simplifying complex tasks like pixel-level classification without Python dependencies. This tool enhances Earth observation data processing for researchers.

Area Of Science

  • Geospatial data science
  • Earth observation
  • Geophysical data analysis

Background

  • Deep learning (DL) methods, particularly Convolutional Neural Networks (CNNs), excel at analyzing complex spatial data.
  • Existing R packages facilitate geospatial data processing but lack integrated DL capabilities.
  • Implementing DL for geospatial tasks often requires complex Python/PyTorch environments.

Purpose Of The Study

  • Introduce the geodl R package for seamless geospatial deep learning in R.
  • Provide a simplified workflow for pixel-level classification of Earth science data.
  • Enable R users to leverage advanced DL techniques without external dependencies.

Main Methods

  • Leverages the torch package for DL implementation within R, integrating C++.
  • Utilizes the terra package for efficient processing of large-scale, multi-band raster data.
  • Incorporates the luz package for training loop management and custom DL model components.

Main Results

  • geodl facilitates the creation of raster masks and data subsets for DL model training.
  • The package supports UNet-based models with customizable modules and loss functions.
  • Includes standard remote sensing assessment metrics and enables predictions on extensive spatial datasets.

Conclusions

  • geodl significantly lowers the barrier to entry for applying deep learning to geospatial data within the R environment.
  • The package streamlines the entire workflow from data preprocessing to model assessment.
  • Future development aims to expand functionality and support for diverse geospatial DL applications.

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