使用深度学习和数字图像处理在3D地图上识别建筑物的外部图像并提取高程信息
Identifying the exterior image of buildings on a 3D map and extracting elevation information using deep learning and digital image processing
摘要：
尽管韩国的建筑管理信息长期以来一直提供高质量的信息，但由于其侧重于管理信息，因此信息的实用性水平不高。尽管如此，随着技术的发展，具有更高分辨率的三维（3D）地图已经出现。然而，它不能比视觉传输更好地发挥作用，因为它只包括集中在建筑物外部的图像信息。如果能够从3D地图中提取或识别与建筑物的外观相关的信息，则可以预期该信息的实用性将更有价值，因为国家建筑管理信息随后可以潜在地扩展以包括与建筑物的外观相关的BIM（建筑信息建模）级别的信息。本研究旨在提出并评估一种基本方法，该方法用于提取与建筑物外观相关的信息，以便使用深度学习和数字图像处理进行3D映射。从地图中提取和预处理图像后，使用快速R-CNN（卷积神经元网络区域）模型识别信息。在从地图中提取和预处理图像后，使用Faster R-CNN模型识别信息。结果，它在检测建筑物的标高和窗户部分方面分别显示出约93%和91%的准确度，以及在旨在提取建筑物标高信息的实验中的优异性能。尽管如此，可以预期，通过补充由于实验者对窗口边界不清晰的误解而导致的混合错误检测率或噪声数据的概率，将获得改进的结果。
英文摘要：
Despite the fact that architectural administration information in Korea has been providing high-quality information for a long period of time, the level of utility of the information is not high because it focuses on administrative information. While this is the case, a three-dimensional (3D) map with higher resolution has emerged along with the technological development. However, it cannot function better than visual transmission, as it includes only image information focusing on the exterior of the building. If information related to the exterior of the building can be extracted or identified from a 3D map, it is expected that the utility of the information will be more valuable as the national architectural administration information can then potentially be extended to include such information regarding the building exteriors to the level of BIM(Building Information Modeling). This study aims to present and assess a basic method of extracting information related to the appearance of the exterior of a building for the purpose of 3D mapping using deep learning and digital image processing. After extracting and preprocessing images from the map, information was identified using the Fast R-CNN(Regions with Convolutional Neuron Networks) model. The information was identified using the Faster R-CNN model after extracting and preprocessing images from the map. As a result, it showed approximately 93% and 91% accuracy in terms of detecting the elevation and window parts of the building, respectively, as well as excellent performance in an experiment aimed at extracting the elevation information of the building. Nonetheless, it is expected that improved results will be obtained by supplementing the probability of mixing the false detection rate or noise data caused by the misunderstanding of experimenters in relation to the unclear boundaries of windows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
