Landscape simulation is necessary for stakeholders to discuss future landscapes with new designs in order to preserve good landscapes. Augmented reality can be used to study the future landscape on a large scale by adding a three-dimensional design model to the real world. On the other hand, diminished reality (DR) can simulate the virtual demolition and removal of structures in redevelopment. However, it has not been possible to visually remove moving landscape objects such as vehicles and pedestrians in real time for accurate landscape simulation. This research develops a DR system that can virtually remove moving landscape objects by implementing real-time object detection using deep learning with a game engine, as well as immobile objects such as structures. In addition to evaluating the performance of detecting the size of moving landscape objects, the developed DR system is applied to large-scale landscape simulation at two sites, and its utility is validated.

为了使利益相关者能够使用新设计讨论未来的景观，以保护良好的景观，必须进行景观模拟。通过在现实世界中添加三维设计模型，可以将增强现实用于大规模研究未来景观。另一方面，减少现实（DR）可以模拟重建中的虚拟拆除和结构拆除。然而，不可能实时视觉上移除移动的景观物体，例如车辆和行人，以进行精确的景观模拟。这项研究开发了一种灾难恢复系统，该系统可以通过使用游戏引擎进行深度学习来实现实时对象检测，从而虚拟移除移动的景观对象，还可以检测固定对象（例如结构）。