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

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Aug 21, 2020
Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman

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With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing Maps team has created over 125 highly detailed 3D cities from 11 different countries that cover hundreds of thousands of square kilometer areas. The 3D city models were created using the photogrammetric technique with high-resolution images that were captured from aircraft-mounted cameras. Such a large 3D city database has caught the attention of the US Army for creating virtual simulation environments to support military operations. However, the 3D city models do not have semantic information such as buildings, vegetation, and ground and cannot allow sophisticated user-level and system-level interaction. At I/ITSEC 2019, the authors presented a fully automated data segmentation and object information extraction framework for creating simulation terrain using UAV-based photogrammetric data. This paper discusses the next steps in extending our designed data segmentation framework for segmenting 3D city data. In this study, the authors first investigated the strengths and limitations of the existing framework when applied to the Bing data. The main differences between UAV-based and aircraft-based photogrammetric data are highlighted. The data quality issues in the aircraft-based photogrammetric data, which can negatively affect the segmentation performance, are identified. Based on the findings, a workflow was designed specifically for segmenting Bing data while considering its characteristics. In addition, since the ultimate goal is to combine the use of both small unmanned aerial vehicle (UAV) collected data and the Bing data in a virtual simulation environment, data from these two sources needed to be aligned and registered together. To this end, the authors also proposed a data registration workflow that utilized the traditional iterative closest point (ICP) with the extracted semantic information.

* Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2020  
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