CRAN/E | sits

sits

Satellite Image Time Series Analysis for Earth Observation Data Cubes

Installation

About

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) doi:10.3390/rs13132428. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, and Digital Earth Africa using the Spatio-temporal Asset Catalog (STAC) protocol ( and the 'gdalcubes' R package developed by Appel and Pebesma (2019) doi:10.3390/data4030092. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) doi:10.1016/j.isprsjprs.2021.04.014. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) doi:10.3390/rs11050523, residual networks by Fawaz et al (2019) doi:10.1007/s10618-019-00619-1, and temporal attention encoders by Garnot and Landrieu (2020) doi:10.48550/arXiv.2007.00586. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference, and methods for uncertainty assessment. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) doi:10.1016/j.rse.2014.02.015. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Citation sits citation info
github.com/e-sensing/sits/
e-sensing.github.io/sitsbook/
Bug report File report

Key Metrics

Version 1.5.0
R ≥ 4.0.0
Published 2024-05-09 153 days ago
Needs compilation? yes
License GPL-2
CRAN checks sits results
Language en-US

Downloads

Yesterday 15 -32%
Last 7 days 131 -21%
Last 30 days 656 -16%
Last 90 days 1.896 +6%
Last 365 days 7.213 +51%

Maintainer

Maintainer

Gilberto Camara

Authors

Rolf Simoes

aut

Gilberto Camara

aut / cre

Felipe Souza

aut

Lorena Santos

aut

Pedro Andrade

aut

Karine Ferreira

aut

Alber Sanchez

aut

Gilberto Queiroz

aut

Material

NEWS
Reference manual
Package source

In Views

Spatial

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

sits archive

Depends

R ≥ 4.0.0

Imports

yaml
dplyr ≥ 1.0.0
gdalUtilities
grDevices
graphics
lubridate
parallel ≥ 4.0.5
purrr ≥ 1.0.2
Rcpp
rstac ≥ 1.0.0
sf ≥ 1.0-12
showtext
sysfonts
slider ≥0.2.0
stats
terra ≥ 1.7-71
tibble ≥ 3.1
tidyr ≥1.2.0
torch ≥ 0.11.0
utils

Suggests

aws.s3
caret
cli
covr
dendextend
dtwclust
DiagrammeR
digest
e1071
exactextractr
FNN
future
gdalcubes ≥0.6.0
geojsonsf
ggplot2
httr
jsonlite
kohonen ≥3.0.11
leafem ≥ 0.2.0
leaflet ≥ 2.2.0
luz ≥ 0.4.0
methods
mgcv
nnet
openxlsx
randomForest
randomForestExplainer
RColorBrewer
RcppArmadillo ≥ 0.12
scales
spdep
stars ≥ 0.6
stringr
supercells ≥ 1.0.0
testthat ≥ 3.1.3
tmap ≥ 3.3
torchopt ≥ 0.1.2
tools
xgboost

LinkingTo

Rcpp
RcppArmadillo