Knowledge of accurate digital surface models (DSMs) is a valuable information for security authorities and organizations during emergencies, disasters, or big events. In this context, the potential of the recently developed digital optical camera system (3K) to derive DSMs automatically and in near real time will be evaluated. The 3K camera system with a frame rate of 3Hz consists of three non-metric off-the-shelf cameras (Canon EOS 1Ds Mark II, 16 MPixel), which are aligned in an array with one camera looking in nadir direction and two in oblique direction. The DSMs are calculated using a subpixel hierarchical matching based on interest points followed by a region growing algorithm. Exterior orientation is given online by GPS/IMU data, whereas interior camera parameters are provided in advance. The resulting DSM is calculated by forward intersection. For the validation, the point accuracies in relation to the base-to-height ratio as well as the reached point density in relation to the calculation time were calculated. Two flight campaigns with the 3K camera system were performed with varying frame rates over the centre of Munich on 30th April and on 17th June 2007. Point accuracies over flat terrain were validated with reference DEMs and correspond quite well to simulated accuracies. The validation showed that smaller base-to-height ratios decrease the point accuracies but increase point densities as images are more similar in particular over urban areas. Exemplarily possible applications for near real time DSMs in the context of disaster monitoring are described, e.g. monitoring of land slides, 3D change detection over urban area, and monitoring of infrastructure. Problematic in all cases is the long processing time, thus the matching algorithms must be modified or new techniques must be applied. Some starting points for the reduction of processing times are discussed. In this context, the proposed matching algorithms and results serve as reference for much faster implementations
