The geographic dimension plays a fundamental role in
    multidimensional systems. To define a geographic dimension in a star
    schema, we need a table with attributes corresponding to the levels of
    the dimension. Additionally, we will also need one or more geographic
    layers to represent the data using this dimension. The goal of this
    package is to support the definition of geographic dimensions from
    layers of geographic information related to each other. It makes it
    easy to define relationships between layers and obtain the necessary
    data from them.

Dependency-free, ultra fast calculation of geodesic
    distances.  Includes the reference nanometre-accuracy geodesic
    distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by
    the 'sf' package, as well as Haversine and Vincenty distances. Default
    distance measure is the "Mapbox cheap ruler" which is generally more
    accurate than Haversine or Vincenty for distances out to a few hundred
    kilometres, and is considerably faster. The main function accepts one
    or two inputs in almost any generic rectangular form, and returns
    either matrices of pairwise distances, or vectors of sequential
    distances.

Methods for calculating gradient surface metrics for
    continuous analysis of landscape features.

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'.