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首页> 外文会议>2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing >Disaster detection from aerial imagery with convolutional neural network
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Disaster detection from aerial imagery with convolutional neural network

机译：利用卷积神经网络从航空影像中进行灾害检测

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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，在环境和气候监测领域，对遥感图像的分析是当务之急，主要是为了检测和管理自然灾害。卫星图像或航空图像是有益的，因为它可以广泛捕获地面状况，并在一张卫星图像中提供大量信息。由于近年来获得卫星图像或航空图像变得越来越容易，因此对滑坡检测和洪水检测的需求很高。在本文中，我们提出了卷积神经网络（CNN）来实现自然灾害的自动检测，特别是用于滑坡和洪水检测的自然灾害检测，以更有效地提取灾害特征。 CNN具有强大的阴影功能，能够充分获得灾难的特征，最重要的是能够克服运营商的错误发现或错误判断，从而影响救灾的有效性。神经网络包括两个阶段：训练阶段和测试阶段。我们通过裁剪和调整从Google地球航空影像获得的航空影像并调整其大小，从而创建了灾前和灾后的训练数据补丁。我们目前专注于日本和泰国这两个国家。滑坡和洪水的训练数据集包含50000个斑块。在CNN中对所有补丁进行了训练，以提取发生变化的区域或被称为灾难区域的区域而没有延迟。我们在两次灾难检测中都获得了大约80％-90％的系统精度。基于令人鼓舞的结果，提出的方法可能有助于我们了解深度学习在灾难检测中的作用。

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