Definitely a welcome improvement! Especially useful where the read window does not match natural block size (so the current sparse functionality doesnt help much). It improved the efficiency of a blob extraction algorithm (which requires overlapping windows and thus cannot use natural blocks) we use by around 20-45% depending on our data.
You are able to find the percentage of non null values without reading data is that correct? I don't fully understand how it works yet, but would it be possible to retrieve the indices of non null data? In the case where you data is very sparse but each block still contains a small number of pixels, you would still need to loop through all that null data. If it were possible to retrieve the non null indices that could be useful, at least to speed up python apps... On 8 Jul 2016 4:27 p.m., "Even Rouault" <even.roua...@spatialys.com> wrote: > Hi, > > The topic of sparse dataset management come back regularly, so I've > decided to > tackle it. > > Please find > https://trac.osgeo.org/gdal/wiki/rfc63_sparse_datasets_improvements > for review. > > Even > > -- > Spatialys - Geospatial professional services > http://www.spatialys.com > _______________________________________________ > gdal-dev mailing list > gdal-dev@lists.osgeo.org > http://lists.osgeo.org/mailman/listinfo/gdal-dev
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