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Merge pull request #1136 from e-sensing/dev
Pre-realease 1.5.0-1
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Original file line number | Diff line number | Diff line change |
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@@ -1,6 +1,6 @@ | ||
Package: sits | ||
Type: Package | ||
Version: 1.5.0 | ||
Version: 1.5.0-1 | ||
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]'), | ||
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@@ -17,7 +17,7 @@ Description: An end-to-end toolkit for land use and land cover classification | |
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 | ||
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. | ||
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@@ -28,10 +28,12 @@ Description: An end-to-end toolkit for land use and land cover classification | |
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 uncertainty assessment. Enables best | ||
practices for estimating area and assessing accuracy of land change as | ||
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 | ||
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@@ -58,7 +60,7 @@ Imports: | |
sysfonts, | ||
slider (>= 0.2.0), | ||
stats, | ||
terra (>= 1.7-71), | ||
terra (>= 1.7-65), | ||
tibble (>= 3.1), | ||
tidyr (>= 1.2.0), | ||
torch (>= 0.11.0), | ||
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@@ -130,7 +132,9 @@ Collate: | |
'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' | ||
|
@@ -146,6 +150,7 @@ Collate: | |
'api_mosaic.R' | ||
'api_opensearch.R' | ||
'api_parallel.R' | ||
'api_patterns.R' | ||
'api_period.R' | ||
'api_plot_time_series.R' | ||
'api_plot_raster.R' | ||
|
@@ -215,6 +220,9 @@ Collate: | |
'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' | ||
|
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