This study aims to facilitate a more reliable automated postdisaster assessment of damaged buildings based on the use of multiple view imagery. Toward this, a Multi-View Convolutional Neural Network (MV-CNN) architecture is proposed, which combines the information from different views of a damaged building, resulting in 3-D aggregation of the 2-D damage features from each view. This spatial 3-D context damage information will result in more accurate and reliable damage quantification in the affected buildings. For validation, the presented model is trained and tested on a real-world visual data set of expert-labeled buildings following Hurricane Harvey. The developed model demonstrates an accuracy of 65% in predicting the exact damage states of buildings, and around 81% considering ±1 class deviation from ground-truth, based on a five-level damage scale. Value of information (VOI) analysis reveals that the hybrid models, which consider at least one aerial and ground view, perform better.

中文翻译：

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基于深度多视角图像融合的灾后破坏分类

本研究旨在促进基于多视图图像的使用对受损建筑物进行更可靠的自动化灾后评估。为此，提出了一种多视图卷积神经网络 (MV-CNN) 架构，该架构结合了来自受损建筑物不同视图的信息，从而导致每个视图的 2-D 损坏特征的 3-D 聚合。这种空间 3-D 上下文损坏信息将导致受影响建筑物中更准确和可靠的损坏量化。为了进行验证，所呈现的模型在飓风哈维之后的专家标记建筑物的真实视觉数据集上进行了训练和测试。开发的模型在预测建筑物的确切损坏状态方面表现出 65% 的准确度，考虑到与地面实况的 ±1 级偏差，准确度约为 81%，基于五级伤害等级。信息价值 (VOI) 分析表明，至少考虑一个鸟瞰图和地面视图的混合模型表现更好。