论文标题
bldnet：使用图形卷积网络和城市域知识的半监督变更检测建筑物损坏框架
BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge
论文作者
论文摘要
变更检测对本地损害和了解灾难信息学的破坏至关重要。尽管卷积神经网络是最近变化检测解决方案的核心，但我们在这项工作中提出了Bldnet，这是一种用于建筑损害变化检测的新型图表，并使学习关系以及来自本地模式和非平稳邻里的学习关系和表示形式。更具体地说，我们使用图形卷积网络在半监督的框架中有效地学习这些功能，并具有很少的注释数据。此外，BLDNET公式允许注入其他上下文建筑元功能。我们在XBD数据集上训练和基准测试，以验证我们方法的有效性。我们还在2020年贝鲁特港口爆炸的城市数据上证明了通过结合域知识构建元功能来提高绩效。
Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detection solutions, we present in this work, BLDNet, a novel graph formulation for building damage change detection and enable learning relationships and representations from both local patterns and non-stationary neighborhoods. More specifically, we use graph convolutional networks to efficiently learn these features in a semi-supervised framework with few annotated data. Additionally, BLDNet formulation allows for the injection of additional contextual building meta-features. We train and benchmark on the xBD dataset to validate the effectiveness of our approach. We also demonstrate on urban data from the 2020 Beirut Port Explosion that performance is improved by incorporating domain knowledge building meta-features.
