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With state-of-the-art sensing and photogrammetric techniques, Microsoft BingMaps team has created over 125 highly detailed 3D cities from 11 differentcountries that cover hundreds of thousands of square kilometer areas. The 3Dcity models were created using the photogrammetric technique withhigh-resolution images that were captured from aircraft-mounted cameras. Such alarge 3D city database has caught the attention of the US Army for creatingvirtual simulation environments to support military operations. However, the 3Dcity models do not have semantic information such as buildings, vegetation, andground and cannot allow sophisticated user-level and system-level interaction.At I/ITSEC 2019, the authors presented a fully automated data segmentation andobject information extraction framework for creating simulation terrain usingUAV-based photogrammetric data. This paper discusses the next steps inextending our designed data segmentation framework for segmenting 3D city data.In this study, the authors first investigated the strengths and limitations ofthe existing framework when applied to the Bing data. The main differencesbetween UAV-based and aircraft-based photogrammetric data are highlighted. Thedata quality issues in the aircraft-based photogrammetric data, which cannegatively affect the segmentation performance, are identified. Based on thefindings, a workflow was designed specifically for segmenting Bing data whileconsidering its characteristics. In addition, since the ultimate goal is tocombine the use of both small unmanned aerial vehicle (UAV) collected data andthe Bing data in a virtual simulation environment, data from these two sourcesneeded to be aligned and registered together. To this end, the authors alsoproposed a data registration workflow that utilized the traditional iterativeclosest point (ICP) with the extracted semantic information.

Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data