forked from e-sensing/sits
-
Notifications
You must be signed in to change notification settings - Fork 0
/
DESCRIPTION
268 lines (268 loc) · 7.38 KB
/
DESCRIPTION
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
Package: sits
Type: Package
Version: 1.5.1
Title: Satellite Image Time Series Analysis for Earth Observation Data Cubes
Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = '[email protected]'),
person('Gilberto', 'Camara', role = c('aut', 'cre'), email = '[email protected]'),
person('Felipe', 'Souza', role = c('aut'), email = '[email protected]'),
person('Lorena', 'Santos', role = c('aut'), email = '[email protected]'),
person('Pedro', 'Andrade', role = c('aut'), email = '[email protected]'),
person('Karine', 'Ferreira', role = c('aut'), email = '[email protected]'),
person('Alber', 'Sanchez', role = c('aut'), email = '[email protected]'),
person('Gilberto', 'Queiroz', role = c('aut'), email = '[email protected]')
)
Maintainer: Gilberto Camara <[email protected]>
Description: 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 (<https://stacspec.org/>) 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>.
Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
Performs efficient classification of big Earth observation data cubes and includes
functions for post-classification smoothing based on Bayesian inference, and
methods for active learning and uncertainty assessment. Supports object-based
time series analysis using package supercells <https://jakubnowosad.com/supercells/>.
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.
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.0.0)
URL: https://github.com/e-sensing/sits/, https://e-sensing.github.io/sitsbook/
BugReports: https://github.com/e-sensing/sits/issues
License: GPL-2
ByteCompile: true
LazyData: true
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 (>= 1.7-65),
tibble (>= 3.1),
tidyr (>= 1.2.0),
torch (>= 0.11.0),
units,
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),
magrittr,
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
Config/testthat/edition: 3
Config/testthat/parallel: false
Config/testthat/start-first: cube, raster, regularize, data, ml
LinkingTo:
Rcpp,
RcppArmadillo
RoxygenNote: 7.3.1
Collate:
'api_accessors.R'
'api_accuracy.R'
'api_apply.R'
'api_band.R'
'api_bbox.R'
'api_block.R'
'api_check.R'
'api_chunks.R'
'api_classify.R'
'api_clean.R'
'api_cluster.R'
'api_colors.R'
'api_combine_predictions.R'
'api_comp.R'
'api_conf.R'
'api_csv.R'
'api_cube.R'
'api_data.R'
'api_debug.R'
'api_detect_changes.R'
'api_download.R'
'api_dtw.R'
'api_environment.R'
'api_factory.R'
'api_file_info.R'
'api_file.R'
'api_gdal.R'
'api_gdalcubes.R'
'api_jobs.R'
'api_kohonen.R'
'api_label_class.R'
'api_merge.R'
'api_mixture_model.R'
'api_ml_model.R'
'api_mosaic.R'
'api_opensearch.R'
'api_parallel.R'
'api_patterns.R'
'api_period.R'
'api_plot_time_series.R'
'api_plot_raster.R'
'api_plot_vector.R'
'api_point.R'
'api_predictors.R'
'api_raster.R'
'api_raster_sub_image.R'
'api_raster_terra.R'
'api_reclassify.R'
'api_reduce.R'
'api_regularize.R'
'api_roi.R'
'api_s2tile.R'
'api_samples.R'
'api_segments.R'
'api_select.R'
'api_sf.R'
'api_shp.R'
'api_signal.R'
'api_smooth.R'
'api_smote.R'
'api_som.R'
'api_source.R'
'api_source_aws.R'
'api_source_bdc.R'
'api_source_cdse.R'
'api_source_deafrica.R'
'api_source_hls.R'
'api_source_local.R'
'api_source_mpc.R'
'api_source_sdc.R'
'api_source_stac.R'
'api_source_usgs.R'
'api_space_time_operations.R'
'api_stac.R'
'api_stats.R'
'api_summary.R'
'api_tibble.R'
'api_tile.R'
'api_timeline.R'
'api_torch.R'
'api_torch_psetae.R'
'api_ts.R'
'api_tuning.R'
'api_uncertainty.R'
'api_utils.R'
'api_values.R'
'api_variance.R'
'api_vector.R'
'api_vector_info.R'
'api_view.R'
'RcppExports.R'
'data.R'
'sits-package.R'
'sits_add_base_cube.R'
'sits_apply.R'
'sits_accuracy.R'
'sits_active_learning.R'
'sits_bands.R'
'sits_bbox.R'
'sits_classify.R'
'sits_colors.R'
'sits_combine_predictions.R'
'sits_config.R'
'sits_csv.R'
'sits_cube.R'
'sits_cube_copy.R'
'sits_clean.R'
'sits_cluster.R'
'sits_detect_change.R'
'sits_detect_change_method.R'
'sits_dtw.R'
'sits_factory.R'
'sits_filters.R'
'sits_geo_dist.R'
'sits_get_data.R'
'sits_imputation.R'
'sits_labels.R'
'sits_label_classification.R'
'sits_lighttae.R'
'sits_machine_learning.R'
'sits_merge.R'
'sits_mixture_model.R'
'sits_mlp.R'
'sits_mosaic.R'
'sits_model_export.R'
'sits_patterns.R'
'sits_plot.R'
'sits_predictors.R'
'sits_reclassify.R'
'sits_reduce.R'
'sits_regularize.R'
'sits_resnet.R'
'sits_sample_functions.R'
'sits_segmentation.R'
'sits_select.R'
'sits_sf.R'
'sits_smooth.R'
'sits_som.R'
'sits_summary.R'
'sits_tae.R'
'sits_tempcnn.R'
'sits_timeline.R'
'sits_train.R'
'sits_tuning.R'
'sits_utils.R'
'sits_uncertainty.R'
'sits_validate.R'
'sits_view.R'
'sits_variance.R'
'sits_xlsx.R'
'zzz.R'