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DESCRIPTION
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DESCRIPTION
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Package: geodl
Type: Package
Title: Geospatial semantic segmentation with torch and terra
Version: 0.1.0
Date: 2023-08-03
Author: c(person(family="Maxwell", given="Aaron",
email="Aaron.Maxwell@mail.wvu.edu", role=c("aut", "cre"))
Maintainer: Aaron E. Maxwell <Aaron.Maxwell@mail.wvu.edu>
Description: This package 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 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, M., Sala, E., Schönlieb, C.B. and Rundo, L., 2022. Unified focal loss: Generalising dice and cross entropy-based losses
to handle class imbalanced medical image segmentation. Computerized Medical Imaging and Graphics, 95, p.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 theinstallation of a Python environment. Raster geospatial
data are handled with terra. Models can be trained using a CUDA-enabled GPU; however, multi-GPU training is not supported by torch
in R. Both binary and multiclass models can be trained. This package is still experimental and is a work-in-progress.
Depends: R (>= 4.1)
Imports:
dplyr,
terra,
luz,
sf,
MultiscaleDTM,
psych
License: GPL (>= 3)
URL: https://github.com/maxwell-geospatial/geodl
BugReports: https://github.com/maxwell-geospatial/geodl/issues
NeedsCompilation: no
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Roxygen: list(markdown = TRUE)