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No, the values do not correspond to a probability but rather a confidence region. The 14 values are, a rather arbitrary, set of nominal representations of each of the predefined confidence regions. Analogously, think of a linear regression line with a set of confidence envelopes, with each incremental envelope indicating less certainty in the estimate. In ...


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I export the SLD from QGIS 2.2 and I am running Geoserver 2.6. For the export from QGIS to work, I change the se:SvgParameter into sld:CssParameter. Generally using SDL 1.0 instead of 1.1.0 prove to simplify some of the issues of transferring the symbology form QGIS to Geoserver in my case. But this will depend on the versions of the software you have. ...


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look closely, and you'll see the problem, hiding in plain sight... <!--Parser Error: syntax error, unexpected COLUMN_REF, expecting $end - Expression was: Land use 2 = 'Residential buldings'--> I suspect you'll need to edit the SLD to rename the field name, e.g. "land_use_2". How you do this depends on the format you're importing from (shapefile, ...


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Yes. Differing resolutions will likely cause issues for you later on. I'd suggest either resampling the 30m bands to 15m using a bilinear interpolation, or even pansharpening those bands as well. ArcGIS will not allow you to pansharpen those bands, but using tools in QGis will let you do it.


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The combine function (which is used in foreach) does not store the relevant components into the final randomForest object. See ?randomForest::combine: The confusion, err.rate, mse and rsq components (as well as the corresponding components in the test component, if exist) of the combined object will be NULL. But the predict method returns OOB ...


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You could try and pull up a land use land cover database of your area where that code 0 is located in. It should be the similar one like this http://landcover.usgs.gov/landcoverdata.php but see if you have one the most recent current land use land cover of your area.


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Further to Jeffrey Evans' answer, you could use PDAL to classify the ground points. It's a little harder to get started (on Windows, anyway) but it's free and makes high-volume batch-processing a cinch.


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If the class codes field is not populated there is no way to obtain them. During pre-processing the vendor did not classify the points so, you are functionally dealing with a raw point cloud. Unfortunately, this means that you need to start researching methods for classifying the point cloud yourself. Since you did not provide any context I cannot aim you ...


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this problem arises when we haven't use directly a stacked images, except this we use some processed image like from ATCOR or FLASH generated. If use directly a raw stacked images .img file then this problem doesn't arise


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I actually will remove values above a certain threshold (1.5 Standard deviations above and below the average and rerun interpolation sometimes to show the more subtle differences in the data. I will make one layer that has the outliers still in it with a color scheme like shades of red. Then I will place a transparent outliers-removed version over it so that ...



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