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Disaster detection from aerial imagery with convolutional neural network

机译：卷积神经网络的空中图像灾害检测

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In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%-90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.
机译：近年来，遥感影像的分析是在环境和气候，主要监测的检测和管理自然灾害的应用领域迫切需要。卫星图像或航空图像是有利的，因为它可以广泛地捕捉表面接地的状况和在一块卫星图像的提供的信息的巨量。由于获得卫星图像或航拍图像，近年来越来越方便，滑坡检测和洪水的检测是非常需要的。在本文中，我们提出了自动自然灾害检测特别是山崩和洪水检测通过更有效地提取灾害的特征实现卷积神经网络（CNN）。 CNN是稳健的影子，能够充分获得灾害的特点，最重要的能够通过运营商克服误检测或误判，这将影响到救灾的效果。神经网络由两个阶段：训练阶段和测试阶段。我们创建了由剪裁和调整，从谷歌地球航拍图像获取航空影像培训灾前和灾后的数据补丁。目前，我们的重点是两个国家是日本和泰国。两个山崩和洪水训练数据集包括50000个补丁。所有的补丁在CNN培训，以提取区域，其中的变化发生或称为灾难发生地区刻不容缓。 90 ％，两者灾害检测的 - 我们80％左右获得我们的系统精度。基于有为的结果，所提出的方法可以帮助我们深度学习在灾害检测作用的认识。

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