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Hi,<br>
<br>
I'm working with a patched version of gdal2tiles, which makes use of
parallelization:
<a class="moz-txt-link-freetext" href="http://gis.stackexchange.com/questions/7743/performance-of-google-map-tile-creation-processes/74446#74446">http://gis.stackexchange.com/questions/7743/performance-of-google-map-tile-creation-processes/74446#74446</a><br>
<br>
I want to create a complete TMS cache from raster imagery. No
assumptions about SRS of data type for input data. <br>
<br>
I want the tiling process to be as fast as possible (gdal2tiles is
really heavy process), do you have any recomendations about the data or
the parameters used?<br>
<br>
Right now, I'm doing this<br>
<br>
Step 1: build vrt from input images<br>
<br>
gdal_vrtmerge.py -o merged.vrt <list of input tif files><br>
<br>
Step 2: build tiles from vrt file<br>
<br>
gdal2tiles.py -r cubic -s epsg:XXXX -z 0-19 -w all merged.vrt tms_dir<br>
<br>
Even with parallelization, process still feels really slow. Would it be
faster if, for example, I convert all my input files to epsg:3857? Or if
I scale them to 8-bit? Or if I use near resampling method instead of
cubic? (I'm using cubic because I'm working with continuous data:
satellite images, am I doing it right?). <br>
<br>
Any other tips?<br>
<br>
Many thanks in advance<br>
<br>
Best regards<br>
<br>
<br>
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