Deep learning technologies that use road images to recognize autonomous driving environments have been actively developed. Such deep-learning-based autonomous driving technology needs a large amount of training data that can represent various road, traffic, and weather environments. However, there have been many difficulties in terms of time and cost in collecting training data that can represent various road environments. Therefore, in this study we attempt to build a virtual road environment and develop a system for collecting training data based on the virtual environment. To build a virtual environment identical to the real world, we convert and use two kinds of existing geospatial data: high-definition 3D buildings and high-definition roads. We also develop a system for collecting training data running in the virtual environment. The implementation results of the proposed system show that it is possible to build a virtual environment identical to the real world and to collect specific training data quickly and at any time from the virtual environment with various user-specified settings.