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Classify Pixels Using Deep Learning Produces Empty Output

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a week ago
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dhan
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Emerging Contributor

Hello,

I'm currently attempting to follow this model guide here: Use the model—ArcGIS pretrained models | Documentation

The guide uses this model here: Land Cover Classification (Aerial Imagery) - Overview

This is the tif file that I originally started with:

dhan_0-1753896827413.png

dhan_1-1753896840558.png

However, since this tif file has 4 bands and was in state plane feet, I projected (Project Raster) the layer so that it was in meters and then extracted bands (Extract Bands) 1,2, and 3. That result is shown here:

dhan_4-1753897168364.png

 

dhan_5-1753897179275.png

 

This layer was then used as the input for the Classify Pixels Using Deep Learning tool. The parameters were set as the guide had shown them. This is how I set the tool up:

dhan_6-1753897332529.png

dhan_7-1753897393779.png

 

However, my output from that tool returns a raster layer that only has 0 or 255 values and not the distinct land cover classes. Here is what the output layer properties look like:

dhan_8-1753897739330.png

dhan_9-1753897750812.png

And this is the output layer visualized by stretch:

dhan_10-1753897817612.png


Is there some step I am missing? Or maybe there is some sort of mismatch between my input and what the model expects? 

 

Thanks in advance.

 



 

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