The goal of supercells is to utilize the concept of superpixels to a variety of spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012)), and readapts it to work with arbitrary dissimilarity measures.
You can install the released version of supercells from CRAN with:
install.packages("supercells")You can install the development version from GitHub with:
install.packages("supercells", repos = "https://nowosad.r-universe.dev")library(supercells)
library(terra)
#> terra 1.6.49
library(sf)
#> Linking to GEOS 3.11.0, GDAL 3.5.2, PROJ 9.0.1; sf_use_s2() is TRUE
vol = rast(system.file("raster/volcano.tif", package = "supercells"))
plot(vol)
vol_slic1 = supercells(vol, k = 50, compactness = 1)
plot(vol)
plot(st_geometry(vol_slic1), add = TRUE, lwd = 0.2)
See the package’s vignettes:
- The
supercells()function - Superpixels of a single raster layer
- Superpixels of an RGB raster
- Superpixels of spatial categorical patterns
- Experimental features of the supercells package
Watch the presentations about this package and some related ideas:
- Spatial segmentation in R using the supercells package, 2021-09-02, OpenGeoHub Summer School - slides, video
- Generalizing the Simple Linear Iterative Clustering (SLIC) superpixels, 2021-09-28, GIScience 2021 - slides, video
Read the related article:
- Nowosad, J., Stepinski, T. (2022). Extended SLIC superpixels algorithm for applications to non-imagery geospatial rasters. International Journal of Applied Earth Observation and Geoinformation, https://doi.org/10.1016/j.jag.2022.102935
Contributions to this package are welcome - let us know if you need other distance measures or transformations, have any suggestions, or spotted a bug. The preferred method of contribution is through a GitHub pull request. Feel also free to contact us by creating an issue.