Changes in version 0.3.0 (2025-11-12)                  

  - Support for three model architectures: UNet, UNet with MobileNetv2
    encoder, and UNet3+
  - UNet with MobileNetv2 encoder is no longer limited to three input
    predictor variables
  - Assessment and prediction functions now expect a nn_module object as
    opposed to a luz fitted object
  - Dynamically generate chips during training process as opposed to
    saving them to disk beforehand (still experimental)
  - Ignore outer rows and columns of cells when calculating loss or
    assessment metrics if desired
  - Use R torch to calculate several different land surface parameters
    (LSPs) from a digital terrain model: slope, hillshade, aspect,
    northwardness, eastwardness, transformed solar radiation aspect
    index (TRASP), site exposure index (SEI), topographic position index
    (TPI), and surface curvatures (mean, profile, and planform)
  - Calculate three-band terrain visualization raster grid from a DTM
    using torch or terra
  - New specialized model for extracting geomorphic features from
    digital terrain models (DTMs)
  - New function to count the number of trainable parameters in a model
  - Fixed issue with chip generation pipeline that caused some chips
    with NA cells to be written
  - Updated atrous spatial pyramid pooling (ASPP) module to align with
    the version used within DeepLabv3+

                        Changes in version 0.1.0                        

  - Initial CRAN submission.