# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "SpatialDownscaling" in publications use:' type: software license: GPL-3.0-only title: 'SpatialDownscaling: Methods for Spatial Downscaling Using Deep Learning' version: 0.1.2 doi: 10.32614/CRAN.package.SpatialDownscaling abstract: 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 ) and UNet (Ronneberger et al., 2015 ), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 ). 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) . authors: - family-names: Sipilä given-names: Mika email: mika.e.sipila@jyu.fi orcid: https://orcid.org/0000-0002-5912-840X - family-names: Cappello given-names: Claudia email: claudia.cappello@unisalento.it orcid: https://orcid.org/0000-0002-7905-5068 - family-names: De Iaco given-names: Sandra email: sandra.deiaco@unisalento.it orcid: https://orcid.org/0000-0003-1820-2068 - family-names: Nordhausen given-names: Klaus email: klaus.nordhausen@helsinki.fi orcid: https://orcid.org/0000-0002-3758-8501 - family-names: Taskinen given-names: Sara email: sara.l.taskinen@jyu.fi orcid: https://orcid.org/0000-0001-9470-7258 repository: https://mikasip.r-universe.dev commit: 6a764c1da6a64fbbba030223c67da04f59e364e5 date-released: '2026-01-21' contact: - family-names: Sipilä given-names: Mika email: mika.e.sipila@jyu.fi orcid: https://orcid.org/0000-0002-5912-840X