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Automatic buildings footprint extraction is of great importance to city planning, urban growth management, and landscape visualization. Although traditional building footprint extraction from two-dimensional images is relatively easy, but are often both time-consuming and costly. Automated building footprint extraction from imagery has been studied extensively based on image segmentation using the pixel value, while the other dimension of segmentation, such as height value, have not been fully explored to extract the building footprints that have been used in this paper. This approach uses the Digital Terrain model (DTM) and Digital Surface Model (DSM) generated from the stereo imagery using the panchromatic bands with high spatial resolution to calculate the Normalized Digital Surface Model (nDSM) to separate the features which are above the ground surface. After extraction of Elevation (Height) of each building feature, the image segmentation has been performed to separate the building features using the threshold value. When the building features are separated, the Canny Edge Detection algorithm is used to delineate the actual building boundary. After the extraction of building boundary, it is vectorized. After simplification of the vector data, the building footprints are extracted. The vector data is compared to the digitized data sets, which show that the approach can be consistent and precise as the building segmentation approach has achieved greater accuracy because of incorporation of height value. There is no human error involved in the whole process.