Methods for Spatial Downscaling Using Deep Learning
Overview
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.
- Fixed an incorrect year in the DESCRIPTION
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Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 33%2 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- Help pages citing references
- 29% with a bibliography file
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People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.2Latest2026-01-26 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-26
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.4.0
- Bundled data
- 1.6 MB / 1 file
- Download size
- 1.6 MB
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