[gdal-dev] gdal_polygonize.py TIF to JSON performance

[David Strip]

I ran a test case on my Windows 7 laptop (i7, quad core (not that it
matters), 2.4 GHz, 8G RAM).
Input file was geotiff, 29847x33432, paletted 8-bit, 11 landcover classes.
This dataset covers the city limits of Philadelphia, PA, so the polygon
representing the Delaware River runs approximately from the lower left
to the upper right corner, creating the same pathological case as the
Thames in your dataset. However, unlike your dataset, there are
relatively few other very large features, as would be the flood level
contours in your data set.
gdal_polygonize to shapefile took ~9 hours and the output files sum to
1.22GB, 1.13M ploygonal features.
gdal_polygonize to geojson took about ~15 hours and the output file is
just over 2GB.

Not sure how useful this is in comparing your dataset that started this
thread, but at least it's some point of comparison.