Abstract

The height of a building is a basic characteristic needed for analytical services. It can be used to evaluate the population and functional zoning of a region. The analysis of the height structure of urban territories can be useful for understanding the population dynamics. In this paper, a novel method for determining a building’s height from a single-shot Earth remote sensing oblique image is proposed. The height is evaluated by a simulation algorithm that uses the masks of shadows and the visible parts of the walls. The image is segmented using convolutional neural networks that makes it possible to extract the masks of roofs, shadows, and building walls. The segmentation models are integrated into a completely automatic system for mapping buildings and evaluating their heights. The test dataset containing a labeled set of various buildings is described. The proposed method is tested on this dataset, and it demonstrates the mean absolute error of less than 4 meters.

中文翻译：

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用于从单发卫星图像确定建筑物参数的深度神经网络

摘要

建筑物的高度是分析服务所需的基本特征。它可用于评估区域的人口和功能分区。对城市领地的高度结构进行分析有助于理解人口动态。本文提出了一种通过单次地球遥感倾斜图像确定建筑物高度的新方法。通过模拟算法评估高度，该算法使用阴影遮罩和墙壁的可见部分。使用卷积神经网络对图像进行分割，从而可以提取屋顶，阴影和建筑物墙壁的遮罩。分割模型已集成到用于绘制建筑物和评估其高度的全自动系统中。描述了包含一组标记的各种建筑物的测试数据集。该方法在该数据集上进行了测试，结果表明平均绝对误差小于4米。