update README

e-sensing · Aug 19, 2024 · 32d058c · 32d058c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -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 
@@ -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 

diff --git a/NEWS.md b/NEWS.md
@@ -3,8 +3,9 @@
 # What's new in SITS version 1.5.1
 
 * Support for ESA World Cover map
-* Support for many Digital Earth Australia products
+* 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
@@ -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

diff --git a/README.Rmd b/README.Rmd
@@ -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
@@ -179,7 +172,7 @@ data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 # create a cube from downloaded files
 raster_cube <- sits_cube(
     source = "BDC",
-    collection = "MOD13Q1-6",
+    collection = "MOD13Q1-6.1",
     data_dir = data_dir,
     delim = "_",
     parse_info = c("X1", "X2", "tile", "band", "date"),
@@ -251,7 +244,7 @@ using `sits_view()`.
 data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 sinop <- sits_cube(
     source = "BDC",
-    collection = "MOD13Q1-6",
+    collection = "MOD13Q1-6.1",
     data_dir = data_dir,
     delim = "_",
     parse_info = c("X1", "X2", "tile", "band", "date"),
@@ -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. 
@@ -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