What are you testing Chris? The script or in the texture filter editor?
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testing both (the 3% utilization was during running the script itself, though operantly with the wrong image),What are you testing
ImportGDAL|D:\work\sample-tiff\sample.tif|*|AUTODETECT SplitGrid|LOD15|* DetectFeatures|FTYPE="RASTER"|D:\work\sample-tiff\sample-tif2.tf2|String;VegType|tree|NONE|DONTPROCESSHOLES MergeGrid ExportOGR|VegType="tree"|ESRI Shapefile|D:\work\sample-tiff\Vegetation\sample-tif2.shp|tree
Not sure what you mean, if you change any of the parameters of the SVM step it will automatically retrain. If you change settings on the steps that are input to the SVM step dito.footnote: we could prob use a built-in function/button in texture editor to re-train the image instead of manually jumping from one point to another (for after changing parameter's)
That's my workflow as well. If I find out that a certain area does not work well I add a sample based on that area to ensure that training data for that specific problem is included. So sounds like a good workflow.And I have to say the results were great. I'd say 90-95% accuracy. Again, 7.5 minutes to process. Now, what I see is that I had to add an extra sample on an exact sports field. The script just wasn't deleting veg there without it so maybe this script just needs more attention if it can't find a good match for sample points. I used 22 sample images. Here's a few screenshots of the results on that test image. Below showing how good it filtered out veg on the golf course.
That's a feature I have recently added, it had been on the wishlist for quite a while already.In fact, I saw a warning message in scenProc that I have never seen before:
"Warning - 37 cells contain more than 6000 vegetation objects, this can give issues in the sim" I'm aware of the potential issue, just did not know scenProc was so robust to report on it.
yes, please docan link my test image in a PM to you if you want to try it?
sounds like i did something i shouldn't,Not sure what you mean, if you change any of the parameters of the SVM step it will automatically retrain
it was initially trained and saved, i just swapped the tiff itself as i was trying to fix the projection issues,if you run the texture filter from the script and it is untrained you need to train it
You can't just swap images, the sample images are kept in a scenproc temp folder. So overwriting the file that you have loaded will not change the scenproc copy.it was initially trained and saved, i just swapped the tiff itself as i was trying to fix the projection issues,
kept the image name just coordinates changes in header and auxiliary files, but scenProc didn't like running it before i retrain and save again,
its ok not a big deal ill be more carful if i have to swap physicals files again in the future,