SpatialDownscaling: Methods for Spatial Downscaling Using Deep Learning
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>.
| Version: |
0.1.2 |
| Depends: |
R (≥ 4.4.0) |
| Imports: |
stats, tensorflow, keras3, magrittr, Rdpack, raster, abind |
| Published: |
2026-01-26 |
| DOI: |
10.32614/CRAN.package.SpatialDownscaling (may not be active yet) |
| Author: |
Mika Sipilä  [aut,
cre, cph],
Claudia Cappello
[aut],
Sandra De Iaco
[aut],
Klaus Nordhausen
[aut],
Sara Taskinen
[aut] |
| Maintainer: |
Mika Sipilä <mika.e.sipila at jyu.fi> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.8), TensorFlow, Keras |
| Materials: |
README, NEWS |
| CRAN checks: |
SpatialDownscaling results |
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