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@gilbertocamara
gilbertocamara authored Aug 21, 2024
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6 changes: 3 additions & 3 deletions DESCRIPTION
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Expand Up @@ -18,7 +18,8 @@ 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)
Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia,
NASA HLS 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
Expand All @@ -28,8 +29,7 @@ Description: An end-to-end toolkit for land use and land cover classification
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>.
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
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2 changes: 1 addition & 1 deletion NEWS.md
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Expand Up @@ -5,6 +5,7 @@
* Support for ESA World Cover map
* Support for Digital Earth Australia products
* Support for Digital Earth Africa geomedian products
* Support for PLANET Mosaic products
* Improve .netrc access to Harmonized Landsat-Sentinel cubes
* Use ROI to cut data cube after mosaic operation
* Support for raster and vector classification using DEM as base cubes
Expand All @@ -19,7 +20,6 @@
* Fix torch usage in Apple M3
* Fix date parameter usage in `sits_view()`
* Improve `plot()` performance using raster overviews
* Include support for PLANET Mosaic product

### New features in SITS version 1.5.0
* Support for SENTINEL-1-RTC and SENTINEL-2-L2A in CDSE
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4 changes: 2 additions & 2 deletions R/sits_cube.R
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Expand Up @@ -217,8 +217,8 @@
#' # --- Access to Digital Earth Australia
#' cube_deaustralia <- sits_cube(
#' source = "DEAUSTRALIA",
#' collection = "GA_LS8C_NBART_GM_CYEAR_3",
#' bands = c("BLUE", "GREEN", "RED", "NIR", "SWIR1"),
#' collection = "GA_LS8CLS9C_GM_CYEAR_3",
#' bands = c("RED", "GREEN", "BLUE"),
#' roi = c(
#' lon_min = 137.15991,
#' lon_max = 138.18467,
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59 changes: 9 additions & 50 deletions README.Rmd
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Expand Up @@ -158,13 +158,6 @@ gc_cube <- sits_regularize(
```
The above command builds a regular data cube with all bands interpolated to 60 m spatial resolution and 15-days temporal resolution. Regular data cubes are the input to the `sits` functions for time series retrieval, building machine learning models, and classification of raster images and time series.

The cube can be shown in a leaflet using `sits_view()`.
```{r, echo=TRUE, eval=FALSE}
# View a color composite on a leaflet
sits_view(s2_cube[1, ], green = "B08", blue = "B03", red = "B11")
```


## Working with Time Series in `sits`

### Accessing Time Series in Data Cubes
Expand Down Expand Up @@ -293,42 +286,6 @@ Additionally, the sample quality control methods that use self-organized maps ar

- Lorena Santos, Karine Ferreira, Gilberto Camara, Michelle Picoli, Rolf Simoes, “Quality control and class noise reduction of satellite image time series”. ISPRS Journal of Photogrammetry and Remote Sensing, 177:75-88, 2021. <doi:10.1016/j.isprsjprs.2021.04.014>.

#### Papers that use sits to produce LUCC maps

- Rolf Simoes, Michelle Picoli, et al., "Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017". Sci Data 7(34), 2020. <doi:10.1038/s41597-020-0371-4>.

- Michelle Picoli, Gilberto Camara, et al., “Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculture”. ISPRS Journal of Photogrammetry and Remote Sensing, 2018. <doi:10.1016/j.isprsjprs.2018.08.007>.

- Karine Ferreira, Gilberto Queiroz et al., "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products". Remote Sens. 12:4033, 2020. <doi:10.3390/rs12244033>.

- Hadi, Firman, Laode Muhammad Sabri, Yudo Prasetyo, and Bambang Sudarsono. [Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification](https://doi.org/10.1088/1755-1315/1276/1/012035). In IOP Conference Series: Earth and Environmental Science, 1276:012035. IOP Publishing, 2023.

- Bruno Adorno, Thales Körting, and Silvana Amaral, [Contribution of time-series data cubes to classify urban vegetation types by remote sensing](https://doi.org/10.1016/j.ufug.2022.127817). Urban Forest & Urban Greening, 79, 127817, 2023.

- Giuliani, Gregory. [Time-First Approach for Land Cover Mapping Using Big Earth Observation Data Time-Series in a Data Cube – a Case Study from the Lake Geneva Region (Switzerland)](https://doi.org/10.1080/20964471.2024.2323241). Big Earth Data, 2024.

- Werner, João, Mariana Belgiu et al., [Mapping Integrated Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning](https://doi.org/10.3390/rs16081421). Remote Sensing 16, no. 8 (January 2024): 1421.

#### Papers that describe software used by the sits package

We thank the authors of these papers for making their code available to be used in connection with sits.

- Marius Appel and Edzer Pebesma, “On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library.” Data 4 (3): 1–16, 2020. <doi:10.3390/data4030092>.

- Ron Wehrens and Johannes Kruisselbrink, "Flexible Self-Organising Maps in kohonen 3.0". Journal of Statistical Software, 87(7), 2018. <doi:10.18637/jss.v087.i07>.

- Charlotte Pelletier, Geoffrey I. Webb, and Francois Petitjean. “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.” Remote Sensing 11 (5), 2019. <doi:10.3390/rs11050523>.

- Vivien Garnot, Loic Landrieu, Sebastien Giordano, and Nesrine Chehata, "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention", Conference on Computer Vision and Pattern Recognition, 2020. <doi: 10.1109/CVPR42600.2020.01234>.

- Vivien Garnot, Loic Landrieu, "Lightweight Temporal Self-Attention for Classifying Satellite Images Time Series", 2020. <arXiv:2007.00586>.

- Maja Schneider, Marco Körner, "[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention." ReScience C 7 (2), 2021. <doi:10.5281/zenodo.4835356>.

- Jakub Nowosad, Tomasz Stepinski, "Extended SLIC superpixels algorithm for applications to non-imagery geospatial rasters". International Journal of Applied Earth Observation and Geoinformation, 112, 102935, 2022.

- Martin Tennekes, “tmap: Thematic Maps in R.” Journal of Statistical Software, 84(6), 1–39, 2018.

### Acknowledgements for community support

The authors are thankful for the contributions of Edzer Pebesma, Jakub Nowosad. Marius Appel, Martin Tennekes, Robert Hijmans, Ron Wehrens, and Tim Appelhans, respectively chief developers of the packages `sf`/`stars`, `supercells`, `gdalcubes`, `tmap`, `terra`, `kohonen`, and `leafem`. The `sits` package recognises the great work of the RStudio team, including the `tidyverse`. Many thanks to Daniel Falbel for his great work in the `torch` and `luz` packages. Charlotte Pelletier shared the python code that has been reused for the TempCNN machine learning model. We would like to thank Maja Schneider for sharing the python code that helped the implementation of the `sits_lighttae()` and `sits_tae()` model. We recognise the importance of the work by Chris Holmes and Mattias Mohr on the STAC specification and API.
Expand All @@ -337,19 +294,21 @@ The authors are thankful for the contributions of Edzer Pebesma, Jakub Nowosad.

We acknowledge and thank the project funders that provided financial and material support:

1. Amazon Fund, established by the Brazilian government with financial contribution from Norway, through the project contract between the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology and Space Applications (FUNCATE), for the establishment of the Brazil Data Cube, process 17.2.0536.1.
- Amazon Fund, established by the Brazilian government with financial contribution from Norway, through the project contract between the Brazilian Development Bank (BNDES) and the Foundation for Science, Technology and Space Applications (FUNCATE), for the establishment of the Brazil Data Cube, process 17.2.0536.1.

- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) and from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), for providing MSc and PhD scholarships.

2. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) and from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), for providing MSc and PhD scholarships.
- Sao Paulo Research Foundation (FAPESP) under eScience Program grant 2014/08398-6, for for providing MSc, PhD and post-doc scholarships, equipment, and travel support.

3. Sao Paulo Research Foundation (FAPESP) under eScience Program grant 2014/08398-6, for for providing MSc, PhD and post-doc scholarships, equipment, and travel support.
- International Climate Initiative of the Germany Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (IKI) under grant 17-III-084- Global-A-RESTORE+ (“RESTORE+: Addressing Landscape Restoration on Degraded Land in Indonesia and Brazil”).

4. International Climate Initiative of the Germany Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (IKI) under grant 17-III-084- Global-A-RESTORE+ (“RESTORE+: Addressing Landscape Restoration on Degraded Land in Indonesia and Brazil”).
- Microsoft Planetary Computer under the GEO-Microsoft Cloud Computer Grants Programme.

5. Microsoft Planetary Computer under the GEO-Microsoft Cloud Computer Grants Programme.
- Instituto Clima e Sociedade, under the project grant "Modernization of PRODES and DETER Amazon monitoring systems".

6. The Open-Earth-Monitor Cyberinfratructure project, which has received funding from the European Union's Horizon Europe research and innovation programme under [grant agreement No. 101059548](https://cordis.europa.eu/project/id/101059548).
- The Open-Earth-Monitor Cyberinfratructure project, which has received funding from the European Union's Horizon Europe research and innovation programme under [grant agreement No. 101059548](https://cordis.europa.eu/project/id/101059548).

7. [FAO-EOSTAT](https://www.fao.org/in-action/eostat) initiative, which uses next generation Earth observation tools to produce land cover and land use statistics.
- [FAO-EOSTAT](https://www.fao.org/in-action/eostat) initiative, which uses next generation Earth observation tools to produce land cover and land use statistics.

### How to contribute

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151 changes: 35 additions & 116 deletions README.md
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Expand Up @@ -231,13 +231,6 @@ cubes are the input to the `sits` functions for time series retrieval,
building machine learning models, and classification of raster images
and time series.

The cube can be shown in a leaflet using `sits_view()`.

``` r
# View a color composite on a leaflet
sits_view(s2_cube[1, ], green = "B08", blue = "B03", red = "B11")
```

## Working with Time Series in `sits`

### Accessing Time Series in Data Cubes
Expand Down Expand Up @@ -332,7 +325,7 @@ point_mt_6bands |>

<div class="figure" style="text-align: center">

<img src="man/figures/README-unnamed-chunk-9-1.png" alt="Classification of NDVI time series using TempCNN" />
<img src="man/figures/README-unnamed-chunk-8-1.png" alt="Classification of NDVI time series using TempCNN" />
<p class="caption">
Classification of NDVI time series using TempCNN
</p>
Expand Down Expand Up @@ -380,7 +373,7 @@ plot(label_cube,

<div class="figure" style="text-align: center">

<img src="man/figures/README-unnamed-chunk-10-1.png" alt="Land use and Land cover in Sinop, MT, Brazil in 2018" />
<img src="man/figures/README-unnamed-chunk-9-1.png" alt="Land use and Land cover in Sinop, MT, Brazil in 2018" />
<p class="caption">
Land use and Land cover in Sinop, MT, Brazil in 2018
</p>
Expand All @@ -406,82 +399,6 @@ maps are described in the following reference:
time series”. ISPRS Journal of Photogrammetry and Remote Sensing,
177:75-88, 2021. <doi:10.1016/j.isprsjprs.2021.04.014>.

#### Papers that use sits to produce LUCC maps

- Rolf Simoes, Michelle Picoli, et al., “Land use and cover maps for
Mato Grosso State in Brazil from 2001 to 2017”. Sci Data 7(34), 2020.
<doi:10.1038/s41597-020-0371-4>.

- Michelle Picoli, Gilberto Camara, et al., “Big Earth Observation Time
Series Analysis for Monitoring Brazilian Agriculture”. ISPRS Journal
of Photogrammetry and Remote Sensing, 2018.
<doi:10.1016/j.isprsjprs.2018.08.007>.

- Karine Ferreira, Gilberto Queiroz et al., “Earth Observation Data
Cubes for Brazil: Requirements, Methodology and Products”. Remote
Sens. 12:4033, 2020. <doi:10.3390/rs12244033>.

- Hadi, Firman, Laode Muhammad Sabri, Yudo Prasetyo, and Bambang
Sudarsono. [Leveraging Time-Series Imageries and Open Source Tools for
Enhanced Land Cover
Classification](https://doi.org/10.1088/1755-1315/1276/1/012035). In
IOP Conference Series: Earth and Environmental Science, 1276:012035.
IOP Publishing, 2023.

- Bruno Adorno, Thales Körting, and Silvana Amaral, [Contribution of
time-series data cubes to classify urban vegetation types by remote
sensing](https://doi.org/10.1016/j.ufug.2022.127817). Urban Forest &
Urban Greening, 79, 127817, 2023.

- Giuliani, Gregory. [Time-First Approach for Land Cover Mapping Using
Big Earth Observation Data Time-Series in a Data Cube – a Case Study
from the Lake Geneva Region
(Switzerland)](https://doi.org/10.1080/20964471.2024.2323241). Big
Earth Data, 2024.

- Werner, João, Mariana Belgiu et al., [Mapping Integrated
Crop–Livestock Systems Using Fused Sentinel-2 and PlanetScope Time
Series and Deep Learning](https://doi.org/10.3390/rs16081421). Remote
Sensing 16, no. 8 (January 2024): 1421.

#### Papers that describe software used by the sits package

We thank the authors of these papers for making their code available to
be used in connection with sits.

- Marius Appel and Edzer Pebesma, “On-Demand Processing of Data Cubes
from Satellite Image Collections with the Gdalcubes Library.” Data 4
(3): 1–16, 2020. <doi:10.3390/data4030092>.

- Ron Wehrens and Johannes Kruisselbrink, “Flexible Self-Organising Maps
in kohonen 3.0”. Journal of Statistical Software, 87(7), 2018.
<doi:10.18637/jss.v087.i07>.

- Charlotte Pelletier, Geoffrey I. Webb, and Francois Petitjean.
“Temporal Convolutional Neural Network for the Classification of
Satellite Image Time Series.” Remote Sensing 11 (5), 2019.
<doi:10.3390/rs11050523>.

- Vivien Garnot, Loic Landrieu, Sebastien Giordano, and Nesrine Chehata,
“Satellite Image Time Series Classification with Pixel-Set Encoders
and Temporal Self-Attention”, Conference on Computer Vision and
Pattern Recognition, 2020. \<doi: 10.1109/CVPR42600.2020.01234\>.

- Vivien Garnot, Loic Landrieu, “Lightweight Temporal Self-Attention for
Classifying Satellite Images Time Series”, 2020. \<arXiv:2007.00586\>.

- Maja Schneider, Marco Körner, “\[Re\] Satellite Image Time Series
Classification with Pixel-Set Encoders and Temporal Self-Attention.”
ReScience C 7 (2), 2021. <doi:10.5281/zenodo.4835356>.

- Jakub Nowosad, Tomasz Stepinski, “Extended SLIC superpixels algorithm
for applications to non-imagery geospatial rasters”. International
Journal of Applied Earth Observation and Geoinformation, 112, 102935,
2022.

- Martin Tennekes, “tmap: Thematic Maps in R.” Journal of Statistical
Software, 84(6), 1–39, 2018.

### Acknowledgements for community support

The authors are thankful for the contributions of Edzer Pebesma, Jakub
Expand All @@ -502,37 +419,39 @@ Mattias Mohr on the STAC specification and API.
We acknowledge and thank the project funders that provided financial and
material support:

1. Amazon Fund, established by the Brazilian government with financial
contribution from Norway, through the project contract between the
Brazilian Development Bank (BNDES) and the Foundation for Science,
Technology and Space Applications (FUNCATE), for the establishment
of the Brazil Data Cube, process 17.2.0536.1.

2. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil
(CAPES) and from the Conselho Nacional de Desenvolvimento Científico
e Tecnológico (CNPq), for providing MSc and PhD scholarships.

3. Sao Paulo Research Foundation (FAPESP) under eScience Program grant
2014/08398-6, for for providing MSc, PhD and post-doc scholarships,
equipment, and travel support.

4. International Climate Initiative of the Germany Federal Ministry for
the Environment, Nature Conservation, Building and Nuclear Safety
(IKI) under grant 17-III-084- Global-A-RESTORE+ (“RESTORE+:
Addressing Landscape Restoration on Degraded Land in Indonesia and
Brazil”).

5. Microsoft Planetary Computer under the GEO-Microsoft Cloud Computer
Grants Programme.

6. The Open-Earth-Monitor Cyberinfratructure project, which has
received funding from the European Union’s Horizon Europe research
and innovation programme under [grant agreement
No. 101059548](https://cordis.europa.eu/project/id/101059548).

7. [FAO-EOSTAT](https://www.fao.org/in-action/eostat) initiative, which
uses next generation Earth observation tools to produce land cover
and land use statistics.
- Amazon Fund, established by the Brazilian government with financial
contribution from Norway, through the project contract between the
Brazilian Development Bank (BNDES) and the Foundation for Science,
Technology and Space Applications (FUNCATE), for the establishment of
the Brazil Data Cube, process 17.2.0536.1.

- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil
(CAPES) and from the Conselho Nacional de Desenvolvimento Científico e
Tecnológico (CNPq), for providing MSc and PhD scholarships.

- Sao Paulo Research Foundation (FAPESP) under eScience Program grant
2014/08398-6, for for providing MSc, PhD and post-doc scholarships,
equipment, and travel support.

- International Climate Initiative of the Germany Federal Ministry for
the Environment, Nature Conservation, Building and Nuclear Safety
(IKI) under grant 17-III-084- Global-A-RESTORE+ (“RESTORE+: Addressing
Landscape Restoration on Degraded Land in Indonesia and Brazil”).

- Microsoft Planetary Computer under the GEO-Microsoft Cloud Computer
Grants Programme.

- Instituto Clima e Sociedade, under the project grant “Modernization of
PRODES and DETER Amazon monitoring systems”.

- The Open-Earth-Monitor Cyberinfratructure project, which has received
funding from the European Union’s Horizon Europe research and
innovation programme under [grant agreement
No. 101059548](https://cordis.europa.eu/project/id/101059548).

- [FAO-EOSTAT](https://www.fao.org/in-action/eostat) initiative, which
uses next generation Earth observation tools to produce land cover and
land use statistics.

### How to contribute

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1 change: 1 addition & 0 deletions inst/WORDLIST
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Expand Up @@ -383,6 +383,7 @@ spatio
spatiotemporal
stac
subimage
supercells
svm
tempCNN
tempdir
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3 changes: 2 additions & 1 deletion inst/extdata/cran/check_package_cran.R
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Expand Up @@ -26,7 +26,8 @@ urlchecker::url_update()
# check on other distributions
# _rhub
devtools::check_rhub()
rhub::check_for_cran()
rhub::rhub_doctor("https://github.com/e-sensing/sits")
rhub::rhub_check()
# _win devel
devtools::check_win_devel()
devtools::check_win_release()
Expand Down
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