Package: geodl
Type: Package
Title: Geospatial Semantic Segmentation with Torch and Terra
Version: 0.2.0
Date: 2027-07-22
Authors@R: c(
  person(family="Maxwell", given="Aaron", email="Aaron.Maxwell@mail.wvu.edu", role=c("aut", "cre", "cph")),
  person(family="Farhadpour", given="Sarah", email="sf00039@mix.wvu.edu", role=c("aut")),
  person(family="Das", given="Srinjoy", email="srinjoy.das@mail.wvu.edu", role=c("aut")),
  person(family="Yang", given="Yalin", email="yalin.yang@mail.wvu.edu", role=c("aut")))
Maintainer: Aaron Maxwell <Aaron.Maxwell@mail.wvu.edu>
Description: Provides tools for semantic segmentation of geospatial data using convolutional neural 
  network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image 
  chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks 
  during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a 
  bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user 
  can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also 
  choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the 
  skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections 
  within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or 
  (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after
  Yeung et al. (2022) <doi:10.1016/j.compmedimag.2021.102026>. We have also implemented 
  assessment metrics using the 'luz' package including F1-score, recall, and precision. Trained models can be used to predict to spatial 
  data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package 
  relies on 'torch' for implementing deep learning, which does not require the installation of a 'Python' environment. Raster geospatial 
  data are handled with 'terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); 
  however, multi-GPU training is not supported by 'torch' in 'R'. 
Depends: R (>= 4.1)
Imports: torch (>= 0.11.0), torchvision (>= 0.5.1), dplyr (>= 1.1.3),
        terra (>= 1.7.55), luz (>= 0.4.0), MultiscaleDTM (>= 0.8.2),
        psych (>= 2.3.3), coro (>= 1.0.3), R6 (>= 2.5.1), readr (>=
        2.1.3), rlang (>= 1.1.1)
License: GPL (>= 3)
URL: https://github.com/maxwell-geospatial/geodl,
        https://doi.org/10.31223/X53M6T,
        https://www.wvview.org/geodl/index.html
BugReports: https://github.com/maxwell-geospatial/geodl/issues
NeedsCompilation: no
Encoding: UTF-8
RoxygenNote: 7.3.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Packaged: 2024-08-01 12:46:21 UTC; vidcg
Author: Aaron Maxwell [aut, cre, cph],
  Sarah Farhadpour [aut],
  Srinjoy Das [aut],
  Yalin Yang [aut]
Repository: CRAN
Date/Publication: 2024-08-20 15:00:14 UTC
Built: R 4.6.0; ; 2025-10-14 03:13:16 UTC; windows
