论文标题
使用卫星图像的深度学习自动量化定居损坏
Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images
论文作者
论文摘要
人道主义灾难和政治暴力对我们的生活空间造成了重大破坏。房屋，基础设施和生态系统的赔偿成本通常很难实时量化。实时量化对于既通知救济操作的信息至关重要，又要提前计划重建。在这里，我们在世界各地重大危机前后使用卫星图像来训练强大的基线残留网络（RESNET）和灾难量化金字塔场景解析网络（PSPNET）。 Resnet具有较差的图像质量的鲁棒性，并且可以以高准确性（92 \％）确定破坏区域，而PSPNET则以良好的准确性（84 \％）提供了上下文量化建筑环境损害的量化。由于需要考虑多个损害维度（例如经济损失和死亡），因此我们符合多线性回归模型，以量化整体损害。为了验证我们的深度学习和回归建模的组合系统，我们成功地将预测与2020年贝鲁特港口爆炸的持续恢复相匹配。这些创新可以更好地量化整体灾难幅度，并为聪明的人道主义体系提供灾难的灾难。
Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction with high accuracy (92\%), whereas PSPNet offers contextualised quantification of built environment damage with good accuracy (84\%). As there are multiple damage dimensions to consider (e.g. economic loss and fatalities), we fit a multi-linear regression model to quantify the overall damage. To validate our combined system of deep learning and regression modeling, we successfully match our prediction to the ongoing recovery in the 2020 Beirut port explosion. These innovations provide a better quantification of overall disaster magnitude and inform intelligent humanitarian systems of unfolding disasters.
