Memory-efficient version of r.accumulate
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Updated
May 31, 2020 - C
Memory-efficient version of r.accumulate
GRASS GIS addon: Downloads and imports PROBA-V land cover raster maps from https://zenodo.org/record/3939050
GRASS GIS toolset for the import of digital elevation models (DEMs; German: DOMs). It includes import addons for the open geodata elevation models for Germany.
Iterative version of r.accumulate
GRASS GIS addon, downloads and imports Global Human Settlement data (provided by the JRC, European Commission)
A set of GRASS GIS addons, developed for the HERMOSA (https://hermosa.mundialis.de/) project by mundialis
Example GRASS GIS addon to copy and use to create own addons
GRASS GIS addon for running r.learn.predict in parallel
GRASS GIS addon: Downloads and imports Gong et al. global land cover raster map
GRASS GIS addon t.water.hazard takes an input watermask STRDS generated by t.sentinel_1.water and calculates flood frequency and flood depth maps from it. Related to https://github.com/mundialis/vale/
GRASS GIS toolset for the import of digital orthophotos (DOPs). It includes import addons for the open geodata DOPs for Germany.
GRASS GIS addon for Incora landcover classification. See also https://github.com/mundialis/incora
GRASS GIS addon to check in which German federal state an area of interest is located
GRASS GIS addon. Toolset for detecting water areas in Copernicus Sentinel-1 GRD scenes and registration of the watermasks in a space time dataset. Related to https://github.com/mundialis/vale/
i.sentinel_1 is a GRASS GIS addon toolset that searches, downloads, imports and preprocesses SAR data from the Alaska Satellite Facility using ESA's SNAP software
GRASS GIS worker addon to run r.import in different mapsets.
GRASS GIS addon: Sets the colors for a land cover classification
Fork from bitbucket (https://huhabla@bitbucket.org/huhabla/d_rast_multi.git)
GRASS GIS addon to download and import of ALKIS building data into GRASS DB
GRASS GIS addon v.disaggregate.population: Disaggregation of low resolution population data at administrative boundary level with high-res auxiliary data into a high-res raster with estimated local population
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