[gdal-dev] ogr2ogr reprojection, features are not transformed

Even Rouault even.rouault at mines-paris.org
Wed Nov 16 14:09:19 EST 2011


> It seems that setting source srs is needed when using shapefiles, as
> you said.  This should be documented somewhere (probably on the
> ogr2ogr page and/or shapefile driver page).

Feel free to add a warning. Logically, this should be more in the shapefile 
driver page. But this assumes that people actually read docs, which is dubious 

> The more I use shapefiles the more I see the limitation in this file
> format, and am quite puzzled as to why it is still so widespread...

Yes shapefiles suffers from a lot of deficiencies (limitations of dbf format, no 
native - documented - spatial indexing, prj files, ...) You might experiment 
with spatialite which is far more capable, but still less widespread.

> Any other ideas on how we can fix this?
> Here is how I think it could be done:
> 1- for all EPSG projections, generate its ESRI WKT (and perhaps a few
> variations)
> 2- make a mapping from ESRI WKT (or its hash) to EPSG codes
> 3- use the hash mapping to find the EPSG code from a given WKT.
> Does this make sense?
> An obvious hurdle is that WKTs can have small variations.
> For example,
> EPSG:4618 as output by GDAL:
> $ gdalsrsinfo -o wkt_esri EPSG:4618
> GEOGCS["SAD69",DATUM["D_South_American_1969",SPHEROID["GRS_1967_Truncated",
> 6378160,298.25]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
> whereas an example file (brazil.prj) has:
> GEOGCS["SAD69",DATUM["D_South_American_1969",SPHEROID["GRS_1967_Modified",6
> 378160,298.25]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
> however, GDAL can deal with these variations:
> $ gdalsrsinfo -o wkt_esri ESRI::brazil.prj
> GEOGCS["SAD69",DATUM["D_South_American_1969",SPHEROID["GRS_1967_Truncated",
> 6378160,298.25]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]

The conversion between GDAL WKT and ESRI WKT belongs to the field of 
experimental science certainly. There are some known rules, but a lot of 
particular cases, some still remaining to be unearthed. The version of 
ogr_srs_esri.cpp in 1.8-esri branch is far more complicated than the one in 

As far as your above algorithm is concerned, I'm wondering how it could work, 
with the variations you gave above. Perhaps a statistical approach with fuzzy 
string matching would give better results than something based on hashing ;-) 
More seriously, I think that a campaign of collecting a lot of .PRJ files 
(ideally coming from ESRI software, and not produced by GDAL) would be needed 
first to see which rules can work in practice.

Another point to keep in mind is that the TOWGS84 parameters proposed by GDAL 
do not always make concensus. The GRASS developers are not particularly happy 
with that : they would prefer that a list of possible transformations would be 
proposed when EPSG lists several of them, instead of just one picked up. See 

Best regards,


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