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We have entered an era where the acquisition of 3D data is ubiquitous, continuous, and massive. These data come from multiple sources including high-resolution, geo-corrected imagery from aerial photography and satellites; ground-based close-up images of buildings and urban features; 3D point clouds from airborne laser rangefinding systems, such as Lidar; imagery from synthetic aperture radar; and other sources. To make these data really useful, they should be employed to model the real world, and the model should then be available for interactive exploration and analysis. However, the modeling aspect is not straightforward since almost all the collected data has holes (due to obstructions or poor acquisition conditions), and no single acquisition mode is likely to produce complete models. The overall modeling problem is then one of fusing multisource data consistently and accurately. As acquisition modes are automated and models are produced, there will be an exponential explosion in the amount of data available for analysis and exploration. Based on these needs and issues, we formulated this special issue, which provides a sampling of the latest and most interesting research in several of these areas. The articles included are briefly summarized.