The segmetric Package: Metrics for Assessing Segmentation Accuracy for Geospatial Data

Abstract:

Segmentation methods are a valuable tool for exploring spatial data by identifying objects based on images’ features. However, proper segmentation assessment is critical for obtaining high-quality results and running well-tuned segmentation algorithms Usually, various metrics are used to inform different types of errors that dominate the results. We describe a new R package, segmetric, for assessing and analyzing the geospatial segmentation of satellite images. This package unifies code and knowledge spread across different software implementations and research papers to provide a variety of supervised segmentation metrics available in the literature. It also allows users to create their own metrics to evaluate the accuracy of segmented objects based on reference polygons. We hope this package helps to fulfill some of the needs of the R community that works with Earth Observation data.

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Published

Aug. 26, 2023

Received

Apr 28, 2022

DOI

10.32614/RJ-2023-030

Volume

Pages

15/1

159 - 172


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    Citation

    For attribution, please cite this work as

    Simoes, et al., "The segmetric Package: Metrics for Assessing Segmentation Accuracy for Geospatial Data", The R Journal, 2023

    BibTeX citation

    @article{RJ-2023-030,
      author = {Simoes, Rolf and Sanchez, Alber and Picoli, Michelle C. A. and Meyfroidt, Patrick},
      title = {The segmetric Package: Metrics for Assessing Segmentation Accuracy for Geospatial Data},
      journal = {The R Journal},
      year = {2023},
      note = {https://doi.org/10.32614/RJ-2023-030},
      doi = {10.32614/RJ-2023-030},
      volume = {15},
      issue = {1},
      issn = {2073-4859},
      pages = {159-172}
    }