segmetric
Segmentation Assessment Metrics (segmetric
)
The segmetric
is an open source package that provides a
set of metrics for analyzing and evaluating geospatial segmentations. It
implements 28 supervised metrics used in literature for spatial
segmentation assessment (see References below).
Installation
# install via CRAN
install.packages("segmetric")
Development version
To install the development version of segmetric
, run the
following commands:
# load necessary libraries
library(devtools)
install_github("michellepicoli/segmetric")
Usage
Spatial datasets can be loaded using sf
objects. To
create a segmetric
object, use function
sm_read()
:
library(segmetric)
# load example datasets
data("sample_ref_sf", package = "segmetric")
data("sample_seg_sf", package = "segmetric")
# create segmetric object
m <- sm_read(ref_sf = sample_ref_sf, seg_sf = sample_seg_sf)
Plot your data using plot()
command:
Segmentation metrics can be computed by function
sm_compute()
. Use summary()
to obtain an
overall metric (mean or weighted mean).
# compute AFI metric and summarize it
sm_compute(m, "AFI") %>% summary()
#> [1] -0.007097452
Make multiple calls to compute more other metrics:
# compute OS1, F_measure, and US2 metrics
m <-
sm_compute(m, "OS1") %>%
sm_compute("F_measure") %>%
sm_compute("US2")
# summarize them
summary(m)
#> OS1 F_measure US2
#> 0.17341468 0.84728616 0.08617454
To see all supported metrics, type ?metric_functions
or
run:
# list all supported metrics
sm_list_metrics()
#> [1] "AFI" "D_index" "Dice" "E" "ED3" "F_measure"
#> [7] "Fitness" "IoU" "M" "OI2" "OMerging" "OS1"
#> [13] "OS2" "OS3" "PI" "precision" "qLoc" "QR"
#> [19] "RAsub" "RAsuper" "recall" "RPsub" "RPsuper" "SimSize"
#> [25] "UMerging" "US1" "US2" "US3"
Getting Help
A detailed documentation with examples on how to use each function
inside segmetric
package can be obtained by typing
?segmetric
in R console.
How to contribute?
The segmetric
package was implemented based on an
extensible architecture. Feel free to contribute by implementing new
metrics functions.
- Make a project fork.
- Edit file
R/metric-funs.R
implementing the new
metric.
- Register your metric in
.db_registry()
function at
R/db.R
file using sm_reg_metric()
.
- Make a Pull
Request on the branch dev.
Acknowledgements
This research was supported by the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation program
(Grant agreement No 677140 MIDLAND).
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