makeChipsMultiClass

Generate image chips from images and associated raster masks for multiclass classification

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'.

Aaron Maxwell

geodl

Geospatial Semantic Segmentation with Torch and Terra

Sarah Farhadpour

Srinjoy Das

Yalin Yang

makeChipsMultiClass function

<dl><dt>image</dt>
<dd>SpatRaster object or path to input image. Function will generate a SpatRaster object
internally. The image and mask must have the same extent, number of rows and
columns of pixels, cell size, and coordinate reference system.</dd>
<dt>mask</dt>
<dd>SpatRaster object or path to single-band mask. Function will generate a SpatRaster
object internally. The image and mask must have the same extent, number of
rows and columns of pixels, cell size, and coordinate reference system.</dd>
<dt>n_channels</dt>
<dd>Number of channels in the input image. Default is 3.</dd>
<dt>size</dt>
<dd>Size of image chips as number of rows and columns of pixels.
Default is 256.</dd>
<dt>stride_x</dt>
<dd>Stride in the x (columns) direction. Default is 256.</dd>
<dt>stride_y</dt>
<dd>Stride in the y (rows) direction. Default is 256.</dd>
<dt>outDir</dt>
<dd>Full or relative path to the current working directory where you
want to write the chips to. Subfolders in this directory will be generated by
the function if useExistingDir = FALSE. You must include the final forward
slash in the file path (e.g., "C:/data/chips/").</dd>
<dt>useExistingDir</dt>
<dd>TRUE or FALSE. Write chips into an existing directory
with subfolders already defined as opposed to using a new directory. This can be used
if you want to add chips to an existing set of chips. Default is FALSE.</dd></dl>

Arguments

makeChipsMultiClass — makeChipsMultiClass

<dl>

<dt>image</dt>
<dd>SpatRaster object or path to input image. Function will generate a SpatRaster object
internally. The image and mask must have the same extent, number of rows and
columns of pixels, cell size, and coordinate reference system.</dd>


<dt>mask</dt>
<dd>SpatRaster object or path to single-band mask. Function will generate a SpatRaster
object internally. The image and mask must have the same extent, number of
rows and columns of pixels, cell size, and coordinate reference system.</dd>


<dt>n_channels</dt>
<dd>Number of channels in the input image. Default is 3.</dd>


<dt>size</dt>
<dd>Size of image chips as number of rows and columns of pixels.
Default is 256.</dd>


<dt>stride_x</dt>
<dd>Stride in the x (columns) direction. Default is 256.</dd>


<dt>stride_y</dt>
<dd>Stride in the y (rows) direction. Default is 256.</dd>


<dt>outDir</dt>
<dd>Full or relative path to the current working directory where you
want to write the chips to. Subfolders in this directory will be generated by
the function if useExistingDir = FALSE. You must include the final forward
slash in the file path (e.g., "C:/data/chips/").</dd>


<dt>useExistingDir</dt>
<dd>TRUE or FALSE. Write chips into an existing directory
with subfolders already defined as opposed to using a new directory. This can be used
if you want to add chips to an existing set of chips. Default is FALSE.</dd>

</dl>

makeChipsMultiClass: makeChipsMultiClass

Description

Usage

Value

Arguments

Details

Examples