On Jul 18, 2010, at 6:09 PM, Leith Bade wrote:
> they only have a 32-bit build
Not true.
The complaint that Kakadu has never offered 64 bit versions of their command
line executables for free is false. The Kakadu web site now offers, for free,
version 6.4 for Mac OS X compiled for 32 bit and 64
Unfortunately I can't afford a Kakadu license for personal use so I am using
the precompiled demo binaries. (
http://www.kakadusoftware.com/index.php?option=com_content&task=view&id=26&Itemid=22
)
As you can see they only have a 32-bit build (and it is not even compiled as
Large Address Aware
http
On Jul 18, 2010, at 12:44 AM, Leith Bade wrote:
> kdu_compress also has a memory limit at 2 GB
Not true. Based on your comment, I suspect that you are using a version of
Kakadu that is more than 3 years old, or that was deliberately compiled for a
32 bit environment.
Since version 6.0, releas
Leith,
For the record, I've just made changes in the development version that allow
specifying more than 2 GB (the effective value might be 2GB or 3GB on 32 bit
operating systems). See http://trac.osgeo.org/gdal/ticket/3689
And the current limit is 2047, not 2147. (2147 * 1024 * 1024 is a negat
That seemed to do the trick. It finished overnight, a lot faster than
before.
Final file was 83GB. (Only 1/2 the dataset was used)
Is there a reason GDAL_CACHEMAX 2147 is the maximum? Under 64-bit a 32-bit
process has 4GB of virtual memory rather than 2GB under 32-bit (or 3GB with
the /3GB Window
Since you have 4 GB RAM, I suggest overriding the 40 MB GDAL_CACHEMAX default,
and increasing it
gdal_merge.py --config GDAL_CACHEMAX 1000
The maximum possible GDAL_CACHEMAX is
gdal_merge.py --config GDAL_CACHEMAX 2147
Greg
On Jul 16, 2010, at 9:19 PM, Leith Bade wrote:
> Hi,
>
> I am trying t
OK I will try the tiling.
The aim of this merge is to generate an input for creating a compressed JPEG
2000 image (either using GDAL, Kakadu tools or the ER Image Compressor).
In the meantime I have installed the 30-day demo of ER Mapper. This program
was able to generate a mosaic in only a few s
I do not see that you specified that the output TIFF image be tiled
-co TILED=YES -co BLOCKXSIZE=512 -co BLOCKYSIZE=512
gdal_merge.py supports the -v input option that reports progress as a % for
every source image merged into the destination image.
It is better to do the initial gdal_merge.py un
Hi,
I am trying to use gdal_merge to mosaic a very large topo GeoTIFF set.
Uncompressed the data set is 60GB, but I keep it stored with DEFLATE
compression which results in a dataset under 10GB.
Mosaicked the uncompressed file will be 125GB because of the large regions
of nodata generated. Unfor
