Importing a large (4.4G) vector map of wetlands takes a long time even on my
8-core/16-thread, 32G desktop. When it finally completed grass recommended
re-importing with an additional 'snap' distance specified:
Command line: > v.in.ogr in=OR_geodatabase_wetlands.gdb out=more_wetlands
loc=geo_wet
On Wed, 11 Sep 2019, Rich Shepard wrote:
Is there a way to put a collection of locations in a single subdirectory,
e.g., /data/grassdata/fishes/ and have grass know how to find them?
Never mind, I figured it out.
I wrote a script to reproject the map from each individual location's
PERMANENT
I imported an OpenFileGDB containing the distributions of 14 species and
runs of fish in the Columbia River Basin. Because one species has a
different CRS than the others each layer needed to be separately imported
into separate locations. This is inefficient and I would like to gather them
in a d
Trying to import an ESRI FileGDB with 10m DEM for Oregon. I don't understand
what I've done incorrectly and would appreciate learning from the grass
command error message.
Input file is OR_DEM_10M.gdb
$ ogrinfo -al -so
Geometry: Multi Polygon
Feature Count: 1
Extent: (-106508955.00, -8540095
On Wed, 11 Sep 2019, Nikos Alexandris wrote:
If there is no need for a GDAL VRT, then, alternatively, link all tiles as
pseudo-raster maps in GRASS GIS' data base, of course using `r.external`.
Then, build a GRASS GIS virtual raster data set using `r.buildvrt`.
Clipping or "extracting" parts of
On Tue, 10 Sep 2019, Helmut Kudrnovsky wrote:
To save diskspace,, build a virtual raster with your tiles outside GRASS
by GDAL's buildvrt, do r.external yourvirtual.vrt.
No import is needed for raster calculations, r.external to link the virtual
raster works nicely..
regarding to clip a raster
* Helmut Kudrnovsky [2019-09-10 20:12:09 -0700]:
Rich Shepard wrote
I am downloading and importing 1m LiDAR DEMs for a 667 mi^2 (1727.5 km^2)
drainage basin. These are all in 7.5' topographic quad sections. The files
take up a lot of disk space and memory when working with them.
I assume that
