Hi,
Is it necessary to show 2 m data at all in the areas where 20-50 cm imagery is
available? If not you can first make tileindex shapefiles from 2 m images and
20-50 cm images. Open both shapefiles with for example OpenJUMP and select and
remove those polygons from 2 m shapefiles which are totally covered by you
20-50 cm tiles. Combine the two layer, save and use the result as a tileindex
file for Mapserver.
There may be problems in the areas with overlapping images. To get the desired
image to show on top it may be necessary to rewrite the shapefile by so that it
will be ordered so that the small pixel size images will be written last in the
shapefile. Then they should be drawn on top of 2 m imagery.
If you want the show 2 m images for the whole area when zoomed far away,
prepare still the combined shapefile with covered by -images removed. Then
make two scale dependent layers under the same group. Read 2 m data when scale
is between 200k and 100k and switch then to the combined layer.
There may be better ways, this is the best I have invented this far for our own
use.
Take care of creating enough many low-resolution layers with gdaladdo for far
away zooms. Even then if Mapserver must open more than 20-40 image files for
filling the screen I would consider making a separate overview image by
mosaicing the whole area together with something like 20-50-100 meter pixel
size and using that image in the same image group.
-Jukka Rahkonen-
________________________________
Lähettäjä: [email protected]
[mailto:[email protected]] Puolesta Liang Jian
Lähetetty: 16. marraskuuta 2009 13:02
Vastaanottaja: [email protected]
Aihe: [mapserver-users] performance problem with ortho images
Hi all,
I have a problem with mapserver handling ortho images. The situation is
we have 2m data cover the whole country and 20-50cm data cover most cities. We
setup mapfiles as 2m data shows between scale 200k to 500 and 20-50cm data show
between scale 100k to 500. Problem is when requirst data in the city ranges,
mapserver first read data from 20-50cm dataset and then read from 2m dataset
which is unnecessary as the 20-50cm data is enough and the best solution is
when there is no 20-50cm data, 2m data should be used instead.
Do you have any idea how to avoid the unnecessary reading?
Any suggestions will be welcome!
Thanks
/Liang
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