Ground Truthing

Our Remote Sensing lab this week focused on accuracy and ground truthing the classification that we made last week.  We started with our existing LULC classifications and then created a set of sample points.  I recalled an ArcGIS function that creates a fishnet grid over a map and decided to try that as a means to implement systematic sampling.  When the grid was created (as a polyline) ArcGIS also created a set of points in the center of each cell as a separate shapefile.  This, it turns out, was perfect for what I needed as a largely random set of sample points.  I then used Google Street View to zoom in and verify that each point was indeed of the classification that I had assigned.

I ended up with about 90% success and 10% mis-classifications.  The bad classifications tended to be the Industrial areas (not so industrial).  Overall, I think it went pretty well, however and showed that we can pretty well discern the land use classifications of an urban area.