This document presents three algorithms for detecting urban areas from remote sensing imagery: edge density filtering, Pantex texture detection, and Gabor Texture Index filtering. It evaluates these algorithms on a Quickbird image, comparing processing time and accuracy against a reference urban/non-urban mask. The edge density and Gabor Texture Index filters were found to be fastest and most accurate, with Gabor performing slightly better. Applying an NDVI mask first improved results. These algorithms will be integrated into the Orfeo Toolbox for automated urban area extraction.