论文标题
通过图形卷积网络朝着跨柴刀建筑物的破坏评估
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
论文作者
论文摘要
在灾难之后，使用变更检测来计划救援行动，获得了建筑损坏图。当前的卷积神经网络方法没有考虑相邻建筑物之间的相似性来预测损害。我们提出了一种基于图形的新型建筑损伤检测解决方案，以捕获这些关系。我们提出的模型体系结构从当地和邻里功能中学习，以预测建筑物的损害。具体而言，我们采用样本和汇总图卷积策略来学习集合功能，从而概括了看不见的图表，这对于减轻获得新灾害预测所需的时间至关重要。我们在XBD数据集上进行的实验以及与经典卷积神经网络的比较表明，尽管我们的方法受到阶级不平衡的困扰，但在交叉策略概括方面，它具有有希望和独特的优势。
In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations. Current convolutional neural network approaches do not consider the similarities between neighboring buildings for predicting the damage. We present a novel graph-based building damage detection solution to capture these relationships. Our proposed model architecture learns from both local and neighborhood features to predict building damage. Specifically, we adopt the sample and aggregate graph convolution strategy to learn aggregation functions that generalize to unseen graphs which is essential for alleviating the time needed to obtain predictions for new disasters. Our experiments on the xBD dataset and comparisons with a classical convolutional neural network reveal that while our approach is handicapped by class imbalance, it presents a promising and distinct advantage when it comes to cross-disaster generalization.
