Module 6 labs consisted of georeferencing a series of raster data images in the form of aerial photographs to vector data in the form of buildings and roads.
Though scaling and control point linkages in both 1st & 2nd order polynomials, I was able to achieve georeferenced images with RMS values well below 15 and below 10. RMS/RMSE, or root mean square error, is a measure of the differences between the predicted or calculated values and the actual, or observed/measured values (between the data you want to georeference and the data that already exists). The difference between each control point linkage is known as a residual and the RMSE is an aggregation of all the residuals for all linkages between the data.
Once the images were georeferenced, I utilized the editing functions within ArcGISPro to add a new polygon for a building and road that were not present within the vector data. I also added the data attributes prompted by the lab instructions so that would be shown in the final layout as labels.
Finally, I added a multi-ring buffer for the eagle nest location just outside of the UWF campus area I was working with.
My final map layout for the lab exercise:

Comments
Post a Comment