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The paper presents results and analysis of a methodology for automatic building extraction that integrates remote sensing sources and readily available geographic information system data (tax assessor parcel data). The methodology consists of a series of image processing and spatial analysis techniques and incorporates an initial simplification, parcel-based image partitioning procedure. The results showed great promise for such image partitioning in reducing the complexity of the building extraction procedure by localizing its search while maintaining the integrity of the structures (85 percent of buildings reside completely within parcels and the majority of the rest consists of very small portions outside the parcel boundaries). Herein, we discuss the results as they apply to three distinct building types (commercial, residential, and high-rise). The paper presents successful building extraction scenarios as well as challenges, difficulties and drawbacks of the process and identifies the underlying factors that impact the success of footprint extraction for each type of building. The results show that 83 percent of commercial buildings are successfully extracted with average footprint area discrepancy less than 10 percent, while challenges in high-rise and residential scenes are examined and further investigated. [ABSTRACT FROM AUTHOR]