R/data.R

In segmetric: Metrics for Assessing Segmentation Accuracy for Geospatial Data

#' @title LEM+ dataset
#' 
#' @name ref_sf
#'
#' @format 
#' These datasets are objects of class `sf` (inherited from `tbl_df`, 
#' `tbl`, `data.frame`) with 2 variables:
#' \itemize{
#'   \item{`id`: identification}
#'   \item{`geometry`: polygons}
#' }
#' 
#' @source Oldoni et al. (2020) \doi{10.1016/j.dib.2020.106553}.
#' 
#' @references 
#' - Oldoni, L.V., Sanches, I.D.A., Picoli, M.C.A., 
#'   Covre, R.M. and Fronza, J.G., 2020. LEM+ dataset: For 
#'   agricultural remote sensing applications. 
#'   Data in Brief, 33, p.106553.
NULL

#' @rdname ref_sf
#' 
#' @description 
#' `ref_sf`: a dataset containing field boundaries from Luiz Eduardo Magalhaes
#' municipality, Brazil.
#' 
#' The data covers the following extent:
#' xmin: -46.37683 ymin: -12.34579 xmax: -46.15776 ymax: -12.13663
#' CRS: EPSG:4326
#'
#' @format 
#' `ref_sf`: a dataset with 195 features.
#' 
#' @examples
#' data("ref_sf", package = "segmetric")
"ref_sf"


#' @rdname ref_sf
#' 
#' @description 
#' `sample_ref_sf`: a subset of `ref_sf` dataset.
#' 
#' @format 
#' `sample_ref_sf`: a dataset with 5 features.
#' 
#' @examples 
#' data("sample_ref_sf", package = "segmetric")
"sample_ref_sf"

#' @title Segmentation dataset
#' 
#' @name seg_sf
#' 
#' @format 
#' These datasets are objects of class `sf` (inherited from `tbl_df`, 
#' `tbl`, `data.frame`) with 2 variables:
#' \itemize{
#'   \item{`id`: identification}
#'   \item{`geometry`: polygons}
#' }
#' 
#' @references 
#' - Planet Team, 2017. Planet Application Program 
#'   Interface: In Space for Life on Earth. San Francisco, 
#'   CA. <https://www.planet.com>
#'   
#' - Baatz, M., Schape, A., 2000. Multiresolution 
#'   segmentation - an optimization approach for high 
#'   quality multi-scale image segmentation. In: Strobl, J., 
#'   Blaschke, T., Griesebner, G. (Eds.), Angewandte 
#'   Geographische Informations-Verarbeitung XII. 
#'   Wichmann Verlag, Karlsruhe, Germany, pp. 12-23. <>
NULL


#' @rdname seg_sf
#' 
#' @description
#' `seg200_sf`,`seg500_sf`,`seg800_sf`,`seg1000_sf`: a dataset containing 
#' segments generated from PlanetScope image, level 3B, acquired on 
#' Feb 18, 2020, with 3.7-meter resolution (Planet Team, 2017), using the 
#' multiresolution segmentation method (Baatz and Schape, 2000).
#' 
#' The data covers the approximately the same area of LEM+ dataset 
#' (see \link{ref_sf}).
#' 
#' The data was post-processed using the spectral difference algorithm on 
#' band 3.
#' 
#' The polygons were simplified using the Douglas-Peucker algorithm in QGIS. 
#' 
#' Self-intersections were removed using SAGA's Polygon Self-Intersection.
#' 
#' Segmentation parameters:
#' \itemize{
#'   \item{`scale parameter`: 200 (`seg200_sf`), 500 (`seg500_sf`), 
#'   800 (`seg800_sf`), and 1000 (`seg1000_sf`)}
#'   \item{`shape`: 0.9}
#'   \item{`compactness`: 0.1}
#' }
#' 
#' Spectral difference parameters:
#' \itemize{
#'   \item{`spectral difference`: 20}
#' }
#' 
#' Simplification parameter:
#' \itemize{
#'   \item{`distance`: 10-meters}
#' }
#' 
#' Only those polygons intersecting reference data with an area-perimeter ratio 
#' above 25 were selected.
#' 
#' @format 
#' `seg200_sf`: a dataset with 547 features.
#' `seg500_sf`: a dataset with 215 features.
#' `seg800_sf`: a dataset with 169 features.
#' `seg1000_sf`: a dataset with 158 features.
#' 
#' @examples 
#' data("seg200_sf", package = "segmetric")
"seg200_sf"

#' @rdname seg_sf
#' 
#' @examples 
#' data("seg500_sf", package = "segmetric")
"seg500_sf"

#' @rdname seg_sf
#' 
#' @examples 
#' data("seg800_sf", package = "segmetric")
"seg800_sf"

#' @rdname seg_sf
#' 
#' @examples 
#' data("seg1000_sf", package = "segmetric")
"seg1000_sf"

#' @rdname seg_sf
#' 
#' @description
#' `sample_seg_sf`: a subset of `seg_sf` dataset.
#' 
#' @format 
#' `sample_seg_sf`: a dataset with 6 features extracted from 
#' `seg500_sf` dataset.
#' 
#' @examples 
#' data("sample_seg_sf", package = "segmetric")
"sample_seg_sf"