makeCrv

Calculate surface curvatures from input digital terrain model (DTM) using torch

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

makeCrv function

<dl><dt>dtm</dt>
<dd>Input SpatRaster object representing bare earth surface elevations.</dd>
<dt>cellSize</dt>
<dd>Resolution of raster grid. Default is 1 m.</dd>
<dt>mode</dt>
<dd>"mean", "profile", or "planform". Default is "mean".</dd>
<dt>smoothRadius</dt>
<dd>radius of circular moving window. Default is 7 cells.</dd>
<dt>writeRaster</dt>
<dd>TRUE or FALSE. Save output to disk. Default is TRUE.</dd>
<dt>outName</dt>
<dd>Name of output raster with full file path and extension.</dd>
<dt>device</dt>
<dd>"cpu" or "cuda". Use "cuda" for GPU computation. Without using the GPU,
implementation will not be significantly faster than using non-tensor-based computation.
Defaults is "cpu".</dd></dl>

Arguments

makeCrv — makeCrv

<dl>

<dt>dtm</dt>
<dd>Input SpatRaster object representing bare earth surface elevations.</dd>


<dt>cellSize</dt>
<dd>Resolution of raster grid. Default is 1 m.</dd>


<dt>mode</dt>
<dd>"mean", "profile", or "planform". Default is "mean".</dd>


<dt>smoothRadius</dt>
<dd>radius of circular moving window. Default is 7 cells.</dd>


<dt>writeRaster</dt>
<dd>TRUE or FALSE. Save output to disk. Default is TRUE.</dd>


<dt>outName</dt>
<dd>Name of output raster with full file path and extension.</dd>


<dt>device</dt>
<dd>"cpu" or "cuda". Use "cuda" for GPU computation. Without using the GPU,
implementation will not be significantly faster than using non-tensor-based computation.
Defaults is "cpu".</dd>

</dl>

makeCrv: makeCrv

Description

Usage

Arguments

Details

Examples