Having established that land use classification is of particular value in the context of fast-growing cities, we turned our attention to a list of the 10 fastest growing US cities, which are concentrated in sunbelt areas such as Texas, Nevada and Arizona. Other studies such as Goldblatt's \cite{Goldblatt_2018} have generated original training data for urban land use classification through hand-labeling of large raster files. In our research, we instead searched for existing detailed land-use maps in a fast-growing US region. A detailed land-use raster covering the greater Houston area for 2015 was acquired from the Texas GIS website. In Python, the pixel values were reclassified from the existing set of 10 categorical values (where 1-10 represented categories from open water through to dense urban areas, including varieties of non-urban land use such as wetlands and forest) to four categorical values: (1) open water; (2) urban: high density; (3) urban: low density; (4) non-urban land.