论文标题
利用后紫色后卫星图像通过卷积神经网络识别洪水损害
Utilizing Post-Hurricane Satellite Imagery to Identify Flooding Damage with Convolutional Neural Networks
论文作者
论文摘要
纯粹的后损害评估对于管理资源分配和执行有效的响应至关重要。传统上，这项评估是通过野外侦察进行的，该侦察速度缓慢，危险且艰巨。取而代之的是，在本文中，我们进一步提出了通过卷积神经网络实施深度学习的想法，以便将建筑物后的赫里奇卫星卫星图像分类为被洪水/损坏或未损害的建筑物。该实验是采用一个数据集进行的，该数据集在2017年哈维飓风之后，该数据集在大休斯顿地区的紫色后卫星图像进行。本文实施了三个卷积神经网络模型体系结构，并配对了其他模型考虑，以实现高精度（超过99％），从而增强了机器学习后在救生后救生灾害中的有效使用。
Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead, in this paper we furthered the idea of implementing deep learning through convolutional neural networks in order to classify post-hurricane satellite imagery of buildings as Flooded/Damaged or Undamaged. The experimentation was conducted employing a dataset containing post-hurricane satellite imagery from the Greater Houston area after Hurricane Harvey in 2017. This paper implemented three convolutional neural network model architectures paired with additional model considerations in order to achieve high accuracies (over 99%), reinforcing the effective use of machine learning in post-hurricane disaster assessment.
