This paper presents SiteCity, a semi-automated building extraction system integrating photogrammetry, geometric constraints and image understanding algorithms. Existing automated building extraction systems produce mixed results and it is clear that human intervention is required to correct mistakes from fully automated systems. SiteCity gives human operators the ability to construct and manipulate three dimensional building objects using multiple images. Image understanding algorithms are integrated into SiteCity to assist users. The automated processes in SiteCity use user-delineated roof boundaries as cues, and attempt to locate the floor of a building and match the building object in other images. In addition, photogrammetric cues are used to assist automated processes. These automated processes are described and their performance is evaluated, illustrating that automated processes in SiteCity produce comparable performance to that of human subjects.

A feature-based stereo matching system that is based on an algorithm for one-dimensional waveform matching is described. It is intended for use in automated cartography, to generate an accurate three-dimensional model of man-made structures and natural terrain. Each epipolar line in the stereo pair is represented as a one-dimensional intensity waveform. The waveform is described as a collection of features, such as peaks and valleys, and represented across a set of hierarchical levels, computed by approximation from the original waveform. These features are matched using an evaluation function that factors similarity of waveform shape, intensity, and symbolic feature description. Waveform matches at coarse resolution are used to constrain matches at finer levels. Intra/inter-scanline corrections are applied and the actual position of the stereo match is adjusted by using the gradient representation of the original waveform. Some representative results are presented for a complex urban scene.>

Three major areas in the development of competent 3-D scene interpretation system are discussed. First, the importance of accurate automatic scene registration and the difficulty in automated extraction and matching of scene reference points are described. Second, the authors describe two stereo matching algorithms, S1, which is an area-based matcher previously used in the SPAM system, and S2, which is a feature-based matching algorithm based on hierarchical waveform matching. Third, the authors introduce several performance evaluation metrics that made it possible to measure the quality of the overall scene recovery, the building disparity estimate, and the quality and sharpness of the building delineations. Such manually generated scene reference models are critical for understanding strengths and weaknesses of various matching algorithms and in the incremental development of improvements to existing algorithms. Experiments were performed on difficult examples of aerial imagery.>

The authors describe a comprehensive stereo analysis system developed to recover the 3D description of an urban area using high-resolution aerial imagery. Given an area of interest in terms of geographic coverage, the system can automatically find the appropriate stereo pair using a spatial database, select control points to register the two images so that epipolar geometry is satisfied, and recover disparity information using two complementary matching techniques. It is believed that stereo mapping research must explicitly address error and uncertainty in both scene registration and stereo matching. In the registration process, five different features can be automatically extracted to select control points for fine image registration. In the stereo matching process two techniques are utilized, an area-based and a feature-based stereo matcher, to generate a disparity map for a scene. Results are shown. An algorithm for stereo matching based upon 1D waveform matching is described. Techniques for generating a rigorous performance analysis to compare stereo matching algorithms on the basis of a manually derived 3D ground truth segmentation are discussed.>