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

ABSTRACT: Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variabl

SUBMITTER: Maxwell AE 

PROVIDER: S-EPMC11620637 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature