论文标题
深度学习的空中图像细分，并带有开放数据进行灾难影响评估
Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment
论文作者
论文摘要
在自然灾害（例如飓风和海啸之后），卫星图像是一种极为宝贵的资源，可以将它们用于风险评估和灾难管理。为了提供及时且可行的信息以进行灾难响应，在本文中，提出了一个利用分割神经网络的框架，以确定污水策划后场景中受影响的区域和可访问的道路。已经分析了ImageNet对航空图像分割任务进行预处理的有效性，并比较了流行分割模型的性能。实验结果表明，在成像网上进行预处理通常会改善许多模型的分割性能。 OpenStreetMap（OSM）可获得的开放数据用于培训，放弃了耗时的手动注释。该方法还利用图理论来更新从OSM获得的道路网络数据，并检测自然灾害引起的变化。对印度尼西亚帕卢（Palu）的2018年海啸数据进行了广泛的实验，显示了拟议框架的有效性。与ENET相比，ENETSEABLABLE，参数少了30％，获得了可比的分割结果与最先进的网络的结果。
Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neural networks is proposed to identify impacted areas and accessible roads in post-disaster scenarios. The effectiveness of pretraining with ImageNet on the task of aerial image segmentation has been analysed and performances of popular segmentation models compared. Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models. Open data available from OpenStreetMap (OSM) is used for training, forgoing the need for time-consuming manual annotation. The method also makes use of graph theory to update road network data available from OSM and to detect the changes caused by a natural disaster. Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer parameters compared to ENet, achieved comparable segmentation results to that of the state-of-the-art networks.
