Rapid 3D Modeling Using Photogrammetry Applied to Google Earth

Low-cost digital photogrammetry using structure-from-motion (SfM) has made it possible for nearly anyone with a digital camera to create dense and precise point cloud models of the physical environment. However, the general requirement for large sets of photos in SfM can present a problem to those with limited resources or limited access to certain locations. This study examined the feasibility of using screen images of Google Earth’s proprietary high-resolution 3D models—not the crowdsourced SketchUp models—in creating point cloud and textured mesh models using SfM. The three study locations included a residential neighborhood in Tokyo, Japan; a portion of the University of California, Santa Barbara (UCSB) campus; and Mount Herard in Colorado. These locations represented a dense urban environment, a mixed environment, and a natural environment, respectively, where Google’s proprietary models existed. Light detection and ranging (LiDAR) data provided an additional data source for evaluating results at UCSB and Mount Herard. Simulated flights were “flown” in Google Earth at each location with screen capture software used to record 45° oblique video of the ground. Individual images were then extracted from the videos and used in Agisoft PhotoScan Professional, an SfM software program, to produce point cloud and textured mesh models of each location. Results of this study support the feasibility of using screen images of Google Earth for SfM modeling. While SfM succeeded in creating models for all three locations that visually resembled Google Earth’s own models, quantitative analysis showed that SfM worked best in the built-up areas of Tokyo and UCSB but struggled with the natural environment of Mount Herard. Comparison of sample distances within the SfM models and Google Earth showed planimetric errors of 1% or less and vertical errors of 5% or less for Tokyo and UCSB; however, absolute errors at Mount Herard—which was compared to LiDAR instead of Google Earth—spanned a range of under 10 m for areas of high relief to values exceeding 100 m for areas with low relief or low texture. The varying qualities of these models reflected not so much limitations of SfM but its reliance on a number of factors that impacted final model quality, such as image quality and operator skill in performing each step of the SfM workflow.