论文标题
在3D地图上识别建筑物的外观图像，并使用深度学习和数字图像处理提取高程信息
Identifying the exterior image of buildings on a 3D map and extracting elevation information using deep learning and digital image processing
论文作者
论文摘要
尽管韩国的建筑管理信息长期以来一直在提供高质量的信息，但信息的效用水平并不高，因为它专注于行政信息。虽然是这种情况，但与技术发展一起出现了具有更高分辨率的三维（3D）图。但是，它不能比视觉传输更好，因为它仅包含针对建筑物外部的图像信息。如果可以从3D地图中提取或识别与建筑物外部有关的信息，则可以预期该信息的效用将更有价值，因为随后可以扩展国家建筑管理信息，以包括有关建筑物外部到BIM级别（建筑信息建模）的此类信息。这项研究旨在介绍和评估一种基本方法，用于提取与建筑物外观外观相关的信息，目的是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，使用快速的R-CNN（带有卷积神经元网络）模型来识别信息。从地图中提取和预处理图像后，使用更快的R-CNN模型来识别信息。结果，在检测建筑物的高程和窗户部分方面，它显示出约93％和91％的精度，并且在旨在提取建筑物高程信息的实验中表现出色。尽管如此，预计将通过补充混合通过误解实验者对窗户不清楚的界限引起的虚假检测率或噪声数据的可能性来获得改进的结果。
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.
