The building heights of an urban area are useful for space analysis, urban planning, and city management. To this end, a novel method for building height calculation for an urban area is proposed based on street view images and a deep learning model, that is, mask region-based convolutional neural network (Mask R-CNN). First, a spider of street view maps was developed, and an optimization model for observation locations was designed based on a genetic algorithm, by which the street view images of all buildings can be obtained with the minimum number of downloads. Subsequently, a deep learning workflow was designed based on the Mask R-CNN to detect buildings from the panorama images. Finally, an accurate height calculation model considering repeated detection of buildings was developed by mapping between detected buildings and actual buildings. Case studies indicate that the mean error of height calculation is 0.78 m, which achieves high precision for calculating building heights in urban areas, while the average calculation time is 4.57 s per building, which indicates that the proposed method is efficient for the application in urban areas.

中文翻译：

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基于街景图像和深度学习的城区建筑高度计算

市区的建筑物高度对于空间分析、城市规划和城市管理非常有用。为此，提出了一种基于街景图像和深度学习模型的城市建筑高度计算新方法，即基于掩模区域的卷积神经网络（Mask R-CNN）。首先开发了街景地图蜘蛛，并基于遗传算法设计了观测位置优化模型，以最少的下载次数获取所有建筑物的街景图像。随后，基于 Mask R-CNN 设计了深度学习工作流程，以从全景图像中检测建筑物。最后，通过检测到的建筑物与实际建筑物之间的映射，建立了考虑建筑物重复检测的精确高度计算模型。