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https://github.com/nowosad/supercells
The goal of supercells is to utilize the concept of superpixels to a variety of spatial data.
https://github.com/nowosad/supercells
r r-package rspatial rstats spatial
Last synced: 8 days ago
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The goal of supercells is to utilize the concept of superpixels to a variety of spatial data.
- Host: GitHub
- URL: https://github.com/nowosad/supercells
- Owner: Nowosad
- License: gpl-3.0
- Created: 2021-04-27T08:19:18.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-24T13:17:40.000Z (2 months ago)
- Last Synced: 2024-08-25T14:00:44.819Z (2 months ago)
- Topics: r, r-package, rspatial, rstats, spatial
- Language: R
- Homepage: https://jakubnowosad.com/supercells/
- Size: 37.6 MB
- Stars: 65
- Watchers: 6
- Forks: 5
- Open Issues: 13
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# supercells 
[](https://CRAN.R-project.org/package=supercells)
[](https://github.com/Nowosad/supercells/actions)
[](https://app.codecov.io/gh/Nowosad/supercells?branch=master)
[](https://cran.r-project.org/package=supercells)
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.
## Installation
You can install the released version of supercells from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("supercells")
```
You can install the development version from [GitHub](https://github.com/) with:
``` r
install.packages("supercells", repos = "https://nowosad.r-universe.dev")
```
## Example
```{r}
library(supercells)
library(terra)
library(sf)
vol = rast(system.file("raster/volcano.tif", package = "supercells"))
plot(vol)
```
```{r, results='hide'}
vol_slic1 = supercells(vol, k = 50, compactness = 1)
plot(vol)
plot(st_geometry(vol_slic1), add = TRUE, lwd = 0.2)
```
## Documentation
See the package's vignettes:
1. [The `supercells()` function](https://jakubnowosad.com/supercells/articles/main-function.html)
2. [Superpixels of a single raster layer](https://jakubnowosad.com/supercells/articles/articles/one_var.html)
3. [Superpixels of an RGB raster](https://jakubnowosad.com/supercells/articles/articles/rgb_vars.html)
4. [Superpixels of spatial categorical patterns](https://jakubnowosad.com/supercells/articles/articles/motifels.html)
5. [Experimental features of the supercells package](https://jakubnowosad.com/supercells/articles/articles/experimental.html)
Watch the presentations about this package and some related ideas:
1. *Spatial segmentation in R using the supercells package*, 2021-09-02, OpenGeoHub Summer School - [slides](https://jakubnowosad.com/ogh2021/), [video](https://doi.org/10.5446/54880)
2. *Generalizing the Simple Linear Iterative Clustering (SLIC) superpixels*, 2021-09-28, GIScience 2021 - [slides](https://jakubnowosad.com/giscience-2021/), [video](https://www.youtube.com/watch?v=AlyEFkyKLUw&t=2018s)
Read the related article:
1. 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
## Contribution
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](https://github.com/nowosad/supercells/issues).