 This paper presents a new building extraction approach called Building Extraction from LADAR Last Returns, BLLR, that uses LADAR point cloud data to automatically identify and map building footprints with high accuracy for hydrologic applications. The BLLR model includes an absolute difference in elevation, aid, parameter in the local vertical difference filter, VDF, that compares the difference between mean and modal elevations of last returns in each cell. The model is calibrated using building locations compiled by photo analysts and validated using independent building reference data. Performance results indicate that the BLLR model is highly sensitive to concavity in the last boundary tool of LA's tools registered trademark symbol, cell size, and aid threshold values. Properly calibrated, the BIA for two residential sites could be estimated within 1% error for optimized experiments. The BLLR estimated bias were tested using two different types of hydrologic models and found to provide more accurate results than the use of the National Land Cover Database, NLCD, 2011% developed imperviousness data. The VDF developed in this study could be applied to LADAR point cloud filtering algorithms for feature extraction in machine learning or mapping other planar surfaces in more broad-based land cover classifications. This article was authored by Chen Ling-Jee Hung, L. Allen James and Michael E. Hodgson. We are article.tv, links in the description below.