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SpatialDownscaling  

Methods for Spatial Downscaling Using Deep Learning
View on CRAN: Click here


Download and install SpatialDownscaling package within the R console
Install from CRAN:
install.packages("SpatialDownscaling")

Install from Github:
library("remotes")
install_github("cran/SpatialDownscaling")

Install by package version:
library("remotes")
install_version("SpatialDownscaling", "0.1.2")



Attach the package and use:
library("SpatialDownscaling")
Maintained by
Mika Sipilä
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-26
Latest Update: 2026-01-26
Description:
The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.
How to cite:
Mika Sipilä (2026). SpatialDownscaling: Methods for Spatial Downscaling Using Deep Learning. R package version 0.1.2, https://cran.r-project.org/web/packages/SpatialDownscaling. Accessed 13 Sep. 2026.
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