On Fri, Nov 16, 2018 at 4:38 PM JDA <[email protected]> wrote: > > > It's better to use a sensible smoothing method in the first place. I > suppose noone has given this over much thought as in the past you were > ever so happy about every bit of resolution you could get. But in a time > where we get very high resolution LIDAR data, the need to downsample > properly is arising. Look at the interpolation methods: gdalwarp lists > twelve different ones. The first few are for upsampling, and the > remainder mostly for dealing with noisy data. Upsampling is well > covered: cubicspline and lanczos are reasonably sophisticated upsampling > filters, but there is no good downsampling filter. I think this is an > omission, hence my post. The problem is real; downsampling with > 'average' produces artifacts, even from previously upsampled data. > > > There’s a wide array of smoothing options available If you’re willing to work > in python. Based on https://gis.stackexchange.com/a/10467, the basic idea is > to load the raster into a numpy array and then convolve it with either a > kernel of arbitrary size.
There are plenty of different kernel smoothers in GRASS GIS, e.g. box, bartlett, gauss, normal, hermite, sinc, lanczos1, lanczos2, lanczos3, hann, hamming, blackman https://grass.osgeo.org/grass76/manuals/r.resamp.filter.html Using "grass-session" (https://github.com/zarch/grass-session) you can use the functionality in Python also from "outside" without even knowing much about GRASS GIS itself. Markus _______________________________________________ gdal-dev mailing list [email protected] https://lists.osgeo.org/mailman/listinfo/gdal-dev
