Package 'supercells'

Global supercells metrics

Description

Computes global distance diagnostics for supercells

Usage

sc_metrics_global(
  x,
  sc,
  metrics = c("spatial", "value", "combined", "balance"),
  scale = TRUE,
  step,
  compactness,
  dist_fun = NULL
)

Arguments

x	The input SpatRaster used to create sc.
sc	An sf object returned by sc_slic().
metrics	Character vector of metric ideas to return. Options: "spatial", "value", "combined", "balance". Default: c("spatial", "value", "combined", "balance").
scale	Logical. If TRUE, scales spatial and value distances; output columns are named with the ⁠_scaled⁠ suffix.
step	A step value used for the supercells If missing, uses attr(sc, "step") when available
compactness	A compactness value used for the supercells If missing, uses attr(sc, "compactness") when available. Compactness mode is read from attr(sc, "compactness_method") when available.
dist_fun	A distance function name or function, as in sc_slic(). If missing or NULL, uses attr(sc, "dist_fun") when available.

Details

Requires sc with metadata columns (supercells, x, y). If they are missing, they are derived from geometry and row order. Use outcomes = c("supercells", "coordinates", "values") when calling sc_slic() or supercells() to preserve original centers and IDs. Metrics are averaged across supercells (each supercell has equal weight). When using SLIC0 (set compactness = use_adaptive() in sc_slic()), combined and balance metrics use per-supercell adaptive compactness (SLIC0), and scaled value distances are computed with the per-supercell max value distance.

Value

A data.frame with a single row and columns:

step

Step size used to generate supercells. Returned in meters when the input used step = use_meters(...), otherwise in cells.

compactness

Compactness value used to generate supercells; NA for adaptive compactness.

compactness_method

Compactness method: "constant" for fixed compactness, "local_max" for adaptive compactness.

n_supercells

Number of supercells with at least one non-missing pixel.

mean_value_dist / mean_value_dist_scaled

Mean per-supercell value distance from cells to their supercell centers, averaged across supercells. Returned as mean_value_dist (or mean_value_dist_scaled when scale = TRUE). Lower values indicate more homogeneous supercells.

mean_spatial_dist / mean_spatial_dist_scaled

Mean per-supercell spatial distance from cells to their supercell centers, averaged across supercells; units are grid cells (row/column index distance). If the input supercells were created with step = use_meters(...), distances are reported in meters. Returned as mean_spatial_dist (or mean_spatial_dist_scaled when scale = TRUE). Lower values indicate more compact supercells.

mean_combined_dist

Mean per-supercell combined distance, computed from value and spatial distances using compactness and step, averaged across supercells. Returned as mean_combined_dist. Lower values indicate lower overall distance and are mainly useful for ranking.

balance

Mean signed log ratio of scaled value distance to scaled spatial distance (averaged across supercells); 0 indicates balance between value and spatial terms, negative values indicate spatial dominance, and positive values indicate value dominance.

See Also

sc_slic(), sc_metrics_pixels(), sc_metrics_supercells()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_sc = sc_slic(vol, step = 8, compactness = 7)
sc_metrics_global(vol, vol_sc)

---

Pixel-level supercells metrics

Description

Computes per-pixel distance diagnostics from each pixel to its supercell center

Usage

sc_metrics_pixels(
  x,
  sc,
  metrics = c("spatial", "value", "combined", "balance"),
  scale = TRUE,
  step,
  compactness,
  dist_fun = NULL
)

Arguments

x	The input SpatRaster used to create sc.
sc	An sf object returned by sc_slic().
metrics	Character vector of metrics to return. Options: "spatial", "value", "combined", "balance". Default: c("spatial", "value", "combined", "balance").
scale	Logical. If TRUE, returns spatial and value as scaled distances (spatial_scaled, value_scaled).
step	A step value used for the supercells If missing, uses attr(sc, "step") when available
compactness	A compactness value used for the supercells If missing, uses attr(sc, "compactness") when available. Compactness mode is read from attr(sc, "compactness_method") when available.
dist_fun	A distance function name or function, as in sc_slic(). If missing or NULL, uses attr(sc, "dist_fun") when available.

Details

If sc lacks supercells, x, or y columns, they are derived from geometry and row order, which may differ from the original centers. When using SLIC0 (set compactness = use_adaptive() in sc_slic()), combined and balance metrics use per-supercell adaptive compactness (SLIC0), and scaled value distances are computed with the per-supercell max value distance.

Value

A SpatRaster with one or more layers depending on metrics and scale.

spatial

Spatial distance from each pixel to its supercell center in grid-cell units (row/column index distance). If the input supercells were created with step = use_meters(...), distances are reported in meters. Lower values indicate more compact supercells. Returned as spatial_scaled when scale = TRUE.

value

Value distance from each pixel to its supercell center in the raster value space. Lower values indicate more homogeneous supercells. Returned as value_scaled when scale = TRUE.

combined

Combined distance using compactness and step. Overall distance; mainly useful for ranking.

balance

Signed log ratio of scaled value distance to scaled spatial distance. 0 indicates balance; negative values indicate spatial dominance; positive values indicate value dominance. Always computed from scaled components.

See Also

sc_slic(), sc_metrics_supercells(), sc_metrics_global()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_sc = sc_slic(vol, step = 8, compactness = 7)
metrics = sc_metrics_pixels(vol, vol_sc, scale = TRUE)
terra::panel(metrics, nr = 1)

---

Supercell-level metrics

Description

Computes per-supercell distance diagnostics

Usage

sc_metrics_supercells(
  x,
  sc,
  metrics = c("spatial", "value", "combined", "balance"),
  scale = TRUE,
  step,
  compactness,
  dist_fun = NULL
)

Arguments

x	The input SpatRaster used to create sc.
sc	An sf object returned by sc_slic().
metrics	Character vector of metric ideas to return. Options: "spatial", "value", "combined", "balance". Default: c("spatial", "value", "combined", "balance").
scale	Logical. If TRUE, scales spatial and value distances; output columns are named with the ⁠_scaled⁠ suffix.
step	A step value used for the supercells If missing, uses attr(sc, "step") when available
compactness	A compactness value used for the supercells If missing, uses attr(sc, "compactness") when available. Compactness mode is read from attr(sc, "compactness_method") when available.
dist_fun	A distance function name or function, as in sc_slic(). If missing or NULL, uses attr(sc, "dist_fun") when available.

Details

If sc lacks supercells, x, or y columns, they are derived from geometry and row order, which may differ from the original centers. When using SLIC0 (set compactness = use_adaptive() in sc_slic()), combined and balance metrics use per-supercell adaptive compactness (SLIC0), and scaled value distances are computed with the per-supercell max value distance.

Value

An sf object with one row per supercell and columns:

supercells

Supercell ID.

mean_spatial_dist

Mean spatial distance from cells to the supercell center in grid-cell units (row/column index distance). If the input supercells were created with step = use_meters(...), distances are reported in meters. Lower values indicate more compact supercells. Returned as mean_spatial_dist_scaled when scale = TRUE.

mean_value_dist

Mean value distance from cells to the supercell center in value space. Lower values indicate more homogeneous supercells. Returned as mean_value_dist_scaled when scale = TRUE.

mean_combined_dist

Mean combined distance using compactness and step to scale value and spatial distances. Overall distance; mainly useful for ranking.

balance

Signed log ratio of scaled value distance to scaled spatial distance. 0 indicates balance; negative values indicate spatial dominance; positive values indicate value dominance.

See Also

sc_slic(), sc_metrics_pixels(), sc_metrics_global()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_sc = sc_slic(vol, step = 8, compactness = 7)
cl = sc_metrics_supercells(vol, vol_sc)
head(cl)

---

Create supercells

Description

Creates supercells from single- or multi-band rasters using an extended SLIC algorithm. The function supports either a target number of supercells (k) or a fixed grid spacing (step), as well as optional custom centers and chunking.

Usage

sc_slic(
  x,
  step = NULL,
  compactness,
  dist_fun = "euclidean",
  avg_fun = "mean",
  clean = TRUE,
  minarea,
  iter = 10,
  k = NULL,
  centers = NULL,
  outcomes = c("supercells", "coordinates", "values"),
  chunks = FALSE,
  verbose = 0
)

Arguments

x	An object of class SpatRaster (terra) or class stars (stars).
step	Initial center spacing (alternative is k). Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
compactness	A compactness value. Use sc_tune_compactness() to estimate it. Use use_adaptive() to enable adaptive compactness (SLIC0).
dist_fun	A distance function name or a custom function. Supported names: "euclidean", "jsd", "dtw", "dtw2d", or any method from philentropy::getDistMethods(). A custom function must accept two numeric vectors and return a single numeric value.
avg_fun	An averaging function name or custom function used to summarize values within each supercell. Supported names: "mean" and "median". A custom function must accept a numeric vector and return a single numeric value.
clean	Should connectivity of the supercells be enforced?
minarea	Minimal size of a supercell (in cells).
iter	Number of iterations.
k	The number of supercells desired (alternative to step).
centers	Optional sf object of custom centers. Requires step.
outcomes	Character vector controlling which fields are returned. Allowed values are "supercells", "coordinates", and "values". Default is full output (c("supercells", "coordinates", "values")). Use outcomes = "values" for value summaries only.
chunks	Chunking option. Use FALSE for no chunking, TRUE for automatic chunking based on size, or a numeric value for a fixed chunk size (in number of cells per side).
verbose	Verbosity level.

Details

Use sc_slic() for polygon outputs. For raster or point centers outputs, see sc_slic_raster() and sc_slic_points(). Evaluation and diagnostic options:

• Iteration convergence: use sc_slic_convergence() and plot its output.

• Pixel diagnostics: sc_metrics_pixels() for per-pixel spatial, value, and combined distances.

• Cluster diagnostics: sc_metrics_supercells() for per-supercell summaries.

• Global diagnostics: sc_metrics_global() for a single-row summary.

Value

An sf object with the supercell polygons and summary statistics. Information on step, compactness, and compactness_method are attached to the result as attributes (compactness is NA for adaptive compactness).

References

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282. https://doi.org/10.1109/tpami.2012.120

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

See Also

sc_tune_compactness(), use_meters(), use_adaptive(), sc_slic_raster(), sc_slic_points(), sc_slic_convergence(), sc_metrics_pixels(), sc_metrics_supercells(), sc_metrics_global()

Examples

library(supercells)
# One variable

vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_slic1 = sc_slic(vol, step = 8, compactness = 1)
terra::plot(vol)
plot(sf::st_geometry(vol_slic1), add = TRUE, lwd = 0.2)

---

SLIC convergence diagnostics

Description

Runs SLIC and returns per-iteration mean combined distance. The output can be plotted directly with plot().

Usage

sc_slic_convergence(
  x,
  step = NULL,
  compactness,
  dist_fun = "euclidean",
  avg_fun = "mean",
  clean = TRUE,
  minarea,
  iter = 10,
  k = NULL,
  centers = NULL,
  verbose = 0
)

Arguments

x	An object of class SpatRaster (terra) or class stars (stars).
step	Initial center spacing (alternative is k). Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
compactness	A compactness value. Use sc_tune_compactness() to estimate it. Use use_adaptive() to enable adaptive compactness (SLIC0).
dist_fun	A distance function name or a custom function. Supported names: "euclidean", "jsd", "dtw", "dtw2d", or any method from philentropy::getDistMethods(). A custom function must accept two numeric vectors and return a single numeric value.
avg_fun	An averaging function name or custom function used to summarize values within each supercell. Supported names: "mean" and "median". A custom function must accept a numeric vector and return a single numeric value.
clean	Should connectivity of the supercells be enforced?
minarea	Minimal size of a supercell (in cells).
iter	Number of iterations.
k	The number of supercells desired (alternative to step).
centers	Optional sf object of custom centers. Requires step.
verbose	Verbosity level.

Value

A data frame with class sc_slic_convergence and columns:

iter

Iteration number.

mean_distance

Mean combined distance across assigned cells at each iteration.

See Also

sc_slic(), plot()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
conv = sc_slic_convergence(vol, step = 8, compactness = 5, iter = 10)
plot(conv)

---

Get stored sc_slic() parameters

Description

Returns key sc_slic() parameters stored as attributes on a supercells object.

Usage

sc_slic_get_params(sc)

Arguments

sc	An sf object returned by sc_slic().

Value

A one-row data.frame with columns: step, compactness, compactness_method, and dist_fun. The dist_fun column is character; custom distance functions are stored as NA.

See Also

sc_slic(), sc_slic_set_params()

---

Create supercells as points

Description

Runs the SLIC workflow and returns supercell centers as points. Use iter = 0 to return the initial centers before iterations. For polygon outputs, use sc_slic(); for raster output, use sc_slic_raster() By default, returns the same fields as sc_slic(): outcomes = c("supercells", "coordinates", "values"). Use outcomes = "values" for value summaries only.

Usage

sc_slic_points(
  x,
  step = NULL,
  compactness,
  dist_fun = "euclidean",
  avg_fun = "mean",
  clean = TRUE,
  minarea,
  iter = 10,
  k = NULL,
  centers = NULL,
  outcomes = c("supercells", "coordinates", "values"),
  chunks = FALSE,
  verbose = 0
)

Arguments

x	An object of class SpatRaster (terra) or class stars (stars).
step	Initial center spacing (alternative is k). Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
compactness	A compactness value. Use sc_tune_compactness() to estimate it. Use use_adaptive() to enable adaptive compactness (SLIC0).
dist_fun	A distance function name or a custom function. Supported names: "euclidean", "jsd", "dtw", "dtw2d", or any method from philentropy::getDistMethods(). A custom function must accept two numeric vectors and return a single numeric value.
avg_fun	An averaging function name or custom function used to summarize values within each supercell. Supported names: "mean" and "median". A custom function must accept a numeric vector and return a single numeric value.
clean	Should connectivity of the supercells be enforced?
minarea	Minimal size of a supercell (in cells).
iter	Number of iterations.
k	The number of supercells desired (alternative to step).
centers	Optional sf object of custom centers. Requires step.
outcomes	Character vector controlling which fields are returned. Allowed values are "supercells", "coordinates", and "values". Default is full output (c("supercells", "coordinates", "values")). Use outcomes = "values" for value summaries only.
chunks	Chunking option. Use FALSE for no chunking, TRUE for automatic chunking based on size, or a numeric value for a fixed chunk size (in number of cells per side).
verbose	Verbosity level.

Value

An sf object with supercell center points and summary statistics

See Also

sc_slic(), sc_slic_raster()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))

# initial centers (only after local minima placement, no iterations)
init_pts = sc_slic_points(vol, step = 12, compactness = 1, iter = 0)
terra::plot(vol)
plot(sf::st_geometry(init_pts), add = TRUE, pch = 3, col = "red")

# final supercell centers
vol_pts = sc_slic_points(vol, step = 12, compactness = 1)
terra::plot(vol)
plot(sf::st_geometry(vol_pts), add = TRUE, pch = 16, col = "red")

---

Create supercells as a raster

Description

Runs the SLIC workflow and returns a raster of supercell IDs IDs are 1-based and are unique across chunks when chunking is used For polygon outputs, use sc_slic(); for point centers, use sc_slic_points()

Usage

sc_slic_raster(
  x,
  step = NULL,
  compactness,
  dist_fun = "euclidean",
  avg_fun = "mean",
  clean = TRUE,
  minarea,
  iter = 10,
  k = NULL,
  centers = NULL,
  outcomes = "supercells",
  chunks = FALSE,
  verbose = 0
)

Arguments

x	An object of class SpatRaster (terra) or class stars (stars).
step	Initial center spacing (alternative is k). Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
compactness	A compactness value. Use sc_tune_compactness() to estimate it. Use use_adaptive() to enable adaptive compactness (SLIC0).
dist_fun	A distance function name or a custom function. Supported names: "euclidean", "jsd", "dtw", "dtw2d", or any method from philentropy::getDistMethods(). A custom function must accept two numeric vectors and return a single numeric value.
avg_fun	An averaging function name or custom function used to summarize values within each supercell. Supported names: "mean" and "median". A custom function must accept a numeric vector and return a single numeric value.
clean	Should connectivity of the supercells be enforced?
minarea	Minimal size of a supercell (in cells).
iter	Number of iterations.
k	The number of supercells desired (alternative to step).
centers	Optional sf object of custom centers. Requires step.
outcomes	Character vector controlling which fields are returned. Only "supercells" is supported in sc_slic_raster().
chunks	Chunking option. Use FALSE for no chunking, TRUE for automatic chunking based on size, or a numeric value for a fixed chunk size (in number of cells per side).
verbose	Verbosity level.

Value

A SpatRaster with supercell IDs.

See Also

sc_slic(), sc_slic_points()

Examples

library(supercells)
vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_ids = sc_slic_raster(vol, step = 8, compactness = 1)
terra::plot(vol_ids)

---

Set stored sc_slic() parameters

Description

Writes key sc_slic() parameters to attributes on a supercells object.

Usage

sc_slic_set_params(sc, params)

Arguments

sc	An sf object.
params	A data.frame, typically from sc_slic_get_params(). Only the first row is used.

Value

The input object with updated attributes.

See Also

sc_slic(), sc_slic_get_params()

---

Estimate a compactness value

Description

Estimates a compactness value for a chosen raster scale. The current implementation supports one tuning metric, "local_variability", which estimates compactness directly from local value variability.

Usage

sc_tune_compactness(
  raster,
  step = NULL,
  dist_fun = "euclidean",
  metric = "local_variability",
  k = NULL,
  centers = NULL
)

Arguments

raster	A SpatRaster.
step	Initial center spacing (alternative is k). Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
dist_fun	A distance function name or a custom function. Supported names: "euclidean", "jsd", "dtw", "dtw2d", or any method from philentropy::getDistMethods(). A custom function must accept two numeric vectors and return a single numeric value.
metric	Which compactness metric to return. Currently only "local_variability" is supported. The argument is kept for future additions. For "local_variability", compactness = median(local_mean(d_value)) / dim_scale, where local means are computed in windows around initial centers and dim_scale is inferred from the number of raster layers and dist_fun. This keeps compactness adjustable to dimensionality without requiring a separate user-facing scaling argument.
k	The number of supercells desired (alternative to step).
centers	Optional sf object of custom initial centers. Requires step.

Value

A one-row data frame with columns step and compactness.

See Also

sc_slic(), use_meters(), use_adaptive()

Examples

library(terra)
vol = rast(system.file("raster/volcano.tif", package = "supercells"))
tune = sc_tune_compactness(vol, step = 8)
tune$compactness

---

Creates supercells

Description

Creates supercells by bringing the idea of superpixels into spatial analysis and grouping raster cells into coherent regions. It uses a modified version of the SLIC algorithm by Achanta et al. (2012), allowing specification of a distance function.

Usage

supercells(
  x,
  k,
  compactness,
  dist_fun = "euclidean",
  avg_fun = "mean",
  clean = TRUE,
  iter = 10,
  transform = NULL,
  step,
  minarea,
  metadata = TRUE,
  chunks = FALSE,
  verbose = 0
)

Arguments

x	An object of class SpatRaster (terra) or class stars (stars)
k	The number of supercells desired by the user (the output number can be slightly different!). You can use either k or step. It is also possible to provide a set of points (an sf object) as k together with the step value to create custom cluster centers.
compactness	A compactness value. Larger values cause supercells to be more compact/even (square). Use use_adaptive() to enable adaptive compactness (SLIC0). A compactness value depends on the range of input cell values and selected distance measure.
dist_fun	A distance function. Currently implemented distance functions are "euclidean", "jsd", "dtw" (dynamic time warping), name of any distance function from the philentropy package (see philentropy::getDistMethods(); "log2" is used in this case), or any user defined function accepting two vectors and returning one value. Default: "euclidean"
avg_fun	An averaging function - how the values of the supercells' centers are calculated? The algorithm internally implements common functions "mean" and "median" (provided with quotation marks), but also accepts any fitting R function (e.g., base::mean() or stats::median(), provided as plain function name: mean). Default: "mean". See details for more information.
clean	Should connectivity of the supercells be enforced?
iter	The number of iterations performed to create the output.
transform	Transformation to be performed on the input. By default, no transformation is performed. Currently available transformation is "to_LAB": first, the conversion from RGB to the LAB color space is applied, then the supercells algorithm is run, and afterward, a reverse transformation is performed on the obtained results. (This argument is experimental and may be removed in the future).
step	Initial center spacing. You can use either k or step. Provide a plain numeric value for cell units, or use use_meters() for map-distance steps in meters (automatically converted to cells using raster resolution).
minarea	Specifies the minimal size of a supercell (in cells). Only works when clean = TRUE. By default, when clean = TRUE, average area (A) is calculated based on the total number of cells divided by a number of supercells Next, the minimal size of a supercell equals to A/(2^2) (A is being right shifted)
metadata	Logical. Controls whether metadata columns ("supercells", "x", "y") are included.
chunks	Should the input (x) be processed with chunking before deriving supercells? Use FALSE (default), TRUE (automatic chunking based on size), or a numeric value (chunk side length in number of cells). When TRUE, the memory limit can be set with options(supercells.chunk_mem_gb = 4).
verbose	An integer specifying the level of text messages printed during calculations. 0 means no messages (default), 1 provides basic messages (e.g., calculation stage).

Details

If you want to use additional arguments for the averaging function (avg_fun), you can create a custom function. For example, if you want to calculate the mean by removing missing values, you can use the following code: my_mean = function(x) mean(x, na.rm = TRUE) and then provide avg_fun = my_mean. For raster IDs or point centers outputs, see sc_slic_raster() and sc_slic_points(). For evaluation and diagnostics, see sc_metrics_pixels(), sc_metrics_supercells(), and sc_metrics_global().

Value

An sf object with several columns: (1) supercells - an id of each supercell, (2) y and x coordinates, (3) one or more columns with average values of given variables in each supercell.

References

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282. https://doi.org/10.1109/tpami.2012.120

Nowosad, J. Motif: an open-source R tool for pattern-based spatial analysis. Landscape Ecol (2021). https://doi.org/10.1007/s10980-020-01135-0

See Also

sc_slic()

Examples

library(supercells)
# One variable

vol = terra::rast(system.file("raster/volcano.tif", package = "supercells"))
vol_slic1 = supercells(vol, k = 50, compactness = 1)
terra::plot(vol)
plot(sf::st_geometry(vol_slic1), add = TRUE, lwd = 0.2)

# RGB variables
# ortho = terra::rast(system.file("raster/ortho.tif", package = "supercells"))
# ortho_slic1 = supercells(ortho, k = 1000, compactness = 10, transform = "to_LAB")
# terra::plot(ortho)
# plot(sf::st_geometry(ortho_slic1), add = TRUE)
#
# ### RGB variables - colored output
#
# rgb_to_hex = function(x){
#   apply(t(x), 2, function(x) rgb(x[1], x[2], x[3], maxColorValue = 255))
# }
# avg_colors = rgb_to_hex(sf::st_drop_geometry(ortho_slic1[4:6]))
#
# terra::plot(ortho)
# plot(sf::st_geometry(ortho_slic1), add = TRUE, col = avg_colors)

---

Use adaptive compactness mode

Description

Creates a compactness mode object for adaptive compactness. The "local_max" method corresponds to SLIC0-style local scaling, where compactness is adapted using local maximum value distances.

Usage

use_adaptive(method = "local_max")

Arguments

method

Adaptive compactness method. Currently only "local_max" is supported (SLIC0-style).

Value

An adaptive compactness mode object for compactness arguments.

Examples

use_adaptive()

---

Mark step values as meters

Description

Creates a units value in meters for use in step arguments. Use plain numerics for cell units, and use_meters() for map-distance steps.

Usage

use_meters(x)

Arguments

x	A single positive numeric value.

Value

A units::units object in meters (m).

Examples

use_meters(100)

---