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gilbertocamara committed Oct 29, 2023
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22 changes: 16 additions & 6 deletions README.Rmd
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Expand Up @@ -94,12 +94,16 @@ devtools::install_github("e-sensing/sits", dependencies = TRUE)
library(sits)
```

### Support for GPU

Classification using torch-based deep learning models in `sits` uses CUDA compatible NVIDIA GPUs if available, which provides up 10-fold speed-up compared to using CPUs only. Please see the [installation instructions](https://torch.mlverse.org/docs/articles/installation) for more information on how to install the required drivers.


## Building Earth Observation Data Cubes

### Image Collections Accessible by `sits`

The `sits` package allows users to created data cubes from analysis-ready data (ARD) image collections available in cloud services. The collections accessible in `sits` `r packageVersion("sits")` are:
Users create data cubes from analysis-ready data (ARD) image collections available in cloud services. The collections accessible in `sits` `r packageVersion("sits")` are:

1. Brazil Data Cube ([BDC](http://brazildatacube.org/en/home-page-2/#dataproducts)): Open data collections of Sentinel-2, Landsat-8 and CBERS-4 images.
2. Microsoft Planetary Computer ([MPC](https://planetarycomputer.microsoft.com/catalog)): Open data collection of Sentinel-2/2A and Landsat-8
Expand Down Expand Up @@ -273,7 +277,9 @@ plot(label_cube,

## Additional information

For more information, please see the on-line book ["SITS: Data analysis and machine learning for data cubes using satellite image time series"](https://e-sensing.github.io/sitsbook/).
Since version 1.4.2, `sits` support OBIA analysis of image time series, using an extension of R package `supercells`.

The package is described in detail in on-line book ["SITS: Data analysis and machine learning for data cubes using satellite image time series"](https://e-sensing.github.io/sitsbook/).


### References
Expand Down Expand Up @@ -314,11 +320,15 @@ We thank the authors of these papers for making their code available to be used

- [12] 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>.

#### R packages used in sits
- [13] 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.

- [14] 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 Marius Appel, Tim Appelhans, Henrik Bengtsson, Robert Hijmans, Edzer Pebesma, and Ron Wehrens, respectively chief developers of the packages `gdalcubes`, `leafem`, `data.table`, `terra/raster`, `sf`/`stars`, and `kohonen`. The `sits` package is also much indebted to the work of the RStudio team, including the `tidyverse`. We are indepted to Daniel Falbel for his and the `torch` packages. We thank Charlotte Pelletier and Hassan Fawaz for sharing the python code that has been reused for the TempCNN and ResNet machine learning models. 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.
The authors are thankful for the contributions of Edzer Pebesma, Jakub Novosad. 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 is also much indebted to the work of the RStudio team, including the `tidyverse`. We are indepted to Daniel Falbel for his great work in the `torch` and `luz` packages. We thank Charlotte Pelletier and Hassan Fawaz for sharing the python code that has been reused for the TempCNN and ResNet machine learning models. 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.

## Acknowledgements for Financial and Material Support
### Acknowledgements for Financial and Material Support

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

Expand All @@ -336,7 +346,7 @@ We acknowledge and thank the project funders that provided financial and materia
funding from the European Union's Horizon Europe research and innovation programme
under [grant agreement No. 101059548](https://cordis.europa.eu/project/id/101059548).

## How to contribute
### How to contribute

The `sits` project is released with a [Contributor Code of Conduct](https://github.com/e-sensing/sits/blob/master/CODE_OF_CONDUCT.md).
By contributing to this project, you agree to abide by its terms.
59 changes: 39 additions & 20 deletions README.md
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Expand Up @@ -120,13 +120,21 @@ library(sits)
#> Documentation avaliable in https://e-sensing.github.io/sitsbook/.
```

### Support for GPU

Classification using torch-based deep learning models in `sits` uses
CUDA compatible NVIDIA GPUs if available, which provides up 10-fold
speed-up compared to using CPUs only. Please see the [installation
instructions](https://torch.mlverse.org/docs/articles/installation) for
more information on how to install the required drivers.

## Building Earth Observation Data Cubes

### Image Collections Accessible by `sits`

The `sits` package allows users to created data cubes from
analysis-ready data (ARD) image collections available in cloud services.
The collections accessible in `sits` 1.4.2 are:
Users create data cubes from analysis-ready data (ARD) image collections
available in cloud services. The collections accessible in `sits` 1.4.2
are:

1. Brazil Data Cube
([BDC](http://brazildatacube.org/en/home-page-2/#dataproducts)):
Expand Down Expand Up @@ -381,7 +389,10 @@ Land use and Land cover in Sinop, MT, Brazil in 2018

## Additional information

For more information, please see the on-line book [“SITS: Data analysis
Since version 1.4.2, `sits` support OBIA analysis of image time series,
using an extension of R package `supercells`.

The package is described in detail in on-line book [“SITS: Data analysis
and machine learning for data cubes using satellite image time
series”](https://e-sensing.github.io/sitsbook/).

Expand Down Expand Up @@ -458,23 +469,31 @@ be used in connection with sits.
Self-Attention.” ReScience C 7 (2), 2021.
<doi:10.5281/zenodo.4835356>.

#### R packages used in sits
- \[13\] 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.

- \[14\] 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 Marius Appel, Tim
Appelhans, Henrik Bengtsson, Robert Hijmans, Edzer Pebesma, and Ron
Wehrens, respectively chief developers of the packages `gdalcubes`,
`leafem`, `data.table`, `terra/raster`, `sf`/`stars`, and `kohonen`. The
`sits` package is also much indebted to the work of the RStudio team,
including the `tidyverse`. We are indepted to Daniel Falbel for his and
the `torch` packages. We thank Charlotte Pelletier and Hassan Fawaz for
sharing the python code that has been reused for the TempCNN and ResNet
machine learning models. 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.
The authors are thankful for the contributions of Edzer Pebesma, Jakub
Novosad. 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 is also much indebted to the work of the
RStudio team, including the `tidyverse`. We are indepted to Daniel
Falbel for his great work in the `torch` and `luz` packages. We thank
Charlotte Pelletier and Hassan Fawaz for sharing the python code that
has been reused for the TempCNN and ResNet machine learning models. 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.

## Acknowledgements for Financial and Material Support
### Acknowledgements for Financial and Material Support

We acknowledge and thank the project funders that provided financial and
material support:
Expand Down Expand Up @@ -507,7 +526,7 @@ material support:
and innovation programme under [grant agreement
No. 101059548](https://cordis.europa.eu/project/id/101059548).

## How to contribute
### How to contribute

The `sits` project is released with a [Contributor Code of
Conduct](https://github.com/e-sensing/sits/blob/master/CODE_OF_CONDUCT.md).
Expand Down

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