%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname sits %global packver 1.5.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.5.0 Release: 1%{?dist}%{?buildtag} Summary: Satellite Image Time Series Analysis for Earth Observation Data Cubes License: GPL-2 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.0.0 Requires: R-core >= 4.0.0 BuildRequires: R-parallel >= 4.0.5 BuildRequires: R-CRAN-tibble >= 3.1 BuildRequires: R-CRAN-terra >= 1.7.71 BuildRequires: R-CRAN-tidyr >= 1.2.0 BuildRequires: R-CRAN-purrr >= 1.0.2 BuildRequires: R-CRAN-sf >= 1.0.12 BuildRequires: R-CRAN-dplyr >= 1.0.0 BuildRequires: R-CRAN-rstac >= 1.0.0 BuildRequires: R-CRAN-slider >= 0.2.0 BuildRequires: R-CRAN-torch >= 0.11.0 BuildRequires: R-CRAN-yaml BuildRequires: R-CRAN-gdalUtilities BuildRequires: R-grDevices BuildRequires: R-graphics BuildRequires: R-CRAN-lubridate BuildRequires: R-CRAN-Rcpp BuildRequires: R-CRAN-showtext BuildRequires: R-CRAN-sysfonts BuildRequires: R-stats BuildRequires: R-utils BuildRequires: R-CRAN-RcppArmadillo Requires: R-parallel >= 4.0.5 Requires: R-CRAN-tibble >= 3.1 Requires: R-CRAN-terra >= 1.7.71 Requires: R-CRAN-tidyr >= 1.2.0 Requires: R-CRAN-purrr >= 1.0.2 Requires: R-CRAN-sf >= 1.0.12 Requires: R-CRAN-dplyr >= 1.0.0 Requires: R-CRAN-rstac >= 1.0.0 Requires: R-CRAN-slider >= 0.2.0 Requires: R-CRAN-torch >= 0.11.0 Requires: R-CRAN-yaml Requires: R-CRAN-gdalUtilities Requires: R-grDevices Requires: R-graphics Requires: R-CRAN-lubridate Requires: R-CRAN-Rcpp Requires: R-CRAN-showtext Requires: R-CRAN-sysfonts Requires: R-stats Requires: R-utils %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) . 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 ( and the 'gdalcubes' R package developed by Appel and Pebesma (2019) . 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) . 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) , residual networks by Fawaz et al (2019) , and temporal attention encoders by Garnot and Landrieu (2020) . 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 recommended by Olofsson et al (2014) . Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. %prep %setup -q -c -n %{packname} # fix end of executable files find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; # prevent binary stripping [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true [ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \ sed -i 's@-g0@@g' {} \; || true # don't allow local prefix in executable scripts find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \; %build %install mkdir -p %{buildroot}%{rlibdir} %{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) rm -f %{buildroot}%{rlibdir}/R.css # remove buildroot from installed files find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}