High-fidelity, textured geometric models are a fundamental starting point for computer graphics, simulation, visualization, design, and analysis. Existing tools for acquiring 3D models of large-scale (e.g., urban) geometry from imagery require significant manual input and suffer other, algorithmic scaling limitations. We are pursuing a research and engineering effort to develop a novel sensor, and associated geometric algorithms, to achieve fully automated reconstruction from close-range color images of textured geometric models representing built urban structures.The sensor is a geo-located camera, which annotates each acquired digital image with metadata recording the date and time of image acquisition, and estimating the position and orientation of the acquiring camera in a global (geodetic) coordinate system. This metadata enables the formulation of reconstruction algorithms which scale well both with the number and spatial density of input images, and the complexity of the reconstructed model.We describe our initial dataset of about four thousand geo-located images acquired through a prototype sensor, manual surveying, and semi-automated refinement of navigation information. We demonstrate, for a small office park on the MIT campus, the operation of fully automated algorithms for generating hemispherical image mosaics, for reconstructing vertical building facades, and for estimating high-resolution texture information for each facade. Finally, we describe the status of our efforts, and discuss several significant research and engineering challenges facing the project.