The systematic creation of models of the real world to support the locational awareness on the Internet can be achieved if previously required massive manual labor gets replaced by automated procedures. A particular challenge exists in the automation of the extraction of the 4 classical map features buildings, circulation spaces (e.g. road networks), vegetation and water bodies, as well as their interaction. Decennia of research have been unable to automate the extraction of these features from classical aerial photography towards an economically viable result. However, we believe that we can succeed in the proposed project to develop automated procedures to create feature data for three reasons. First is the recent advent of digital aerial sensors producing highly redundant digital large format aerial photography. Redundancy will be obtained by using high forward and side overlaps, say at 80% and 60%, so that every point in the terrain is imaged at least 10 times, and any algorithm can rely on multiple analysis results that then can either reinforce or cancel one another. Second, the geometric redundancy gets augmented by a radiometric redundancy using 4 spectral bands, adding an infrared band to the classical red, green and blue color channels. Third, we will combine the classical "object reconstruction" approach available from stereo procedures, by new recognition methods. While classically a "car" on a street may have been seen via a "point cloud" and would have to get recognized simply by a representation of local height anomaly on an otherwise flat reference surface, recognition includes the use of stored images of cars in a data base to actually recognize a car as a human would do when inspecting an aerial image. The project is split up into five work packages which will focus on how reconstruction and recognition techniques can help each other and how additional information either from a previous mission or GIS can be integrated in the 3D modeling framework. One work package will address the assessment of the obtained quality, another will address project management and dissemination activities. Within the project we will develop an extensive library of combined recognition/reconstruction methods, and apply them to a range of test data sets. Test data will vary in geometric resolution (pixel size), overlaps, and types of terrain scenarios.