scholarly journals Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

2018 ◽  
Vol 10 (10) ◽  
pp. 1626 ◽  
Author(s):  
Yanbing Bai ◽  
Erick Mas ◽  
Shunichi Koshimura

The satellite remote-sensing-based damage-mapping technique has played an indispensable role in rapid disaster response practice, whereas the current disaster response practice remains subject to the low damage assessment accuracy and lag in timeliness, which dramatically reduces the significance and feasibility of extending the present method to practical operational applications. Therefore, a highly efficient and intelligent remote-sensing image-processing framework is urgently required to mitigate these challenges. In this article, a deep learning algorithm for the semantic segmentation of high-resolution remote-sensing images using the U-net convolutional network was proposed to map the damage rapidly. The algorithm was implemented within a Microsoft Cognitive Toolkit framework in the GeoAI platform provided by Microsoft. The study takes the 2011 Tohoku Earthquake-Tsunami as a case study, for which the pre- and post-disaster high-resolution WorldView-2 image is used. The performance of the proposed U-net model is compared with that of deep residual U-net. The comparison highlights the superiority U-net for tsunami damage mapping in this work. Our proposed method achieves the overall accuracy of 70.9% in classifying the damage into “washed away,” “collapsed,” and “survived” at the pixel level. In future disaster scenarios, our proposed model can generate the damage map in approximately 2–15 min when the preprocessed remote-sensing datasets are available. Our proposed damage-mapping framework has significantly improved the application value in operational disaster response practice by substantially reducing the manual operation steps required in the actual disaster response. Besides, the proposed framework is highly flexible to extend to other scenarios and various disaster types, which can accelerate operational disaster response practice.

2020 ◽  
Vol 12 (2) ◽  
pp. 311 ◽  
Author(s):  
Chun Liu ◽  
Doudou Zeng ◽  
Hangbin Wu ◽  
Yin Wang ◽  
Shoujun Jia ◽  
...  

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.


Author(s):  
Jianfang Shao ◽  
Changyong Liang ◽  
Xihui Wang ◽  
Xiang Wang ◽  
Liang Liang

Demand calculation, which is the base of most logistics decisions and activities, is a critical work in humanitarian logistics (HL). However, previous studies on demand calculation in HL mainly focus on demand forecasting methodology, with many neglecting the checklist of critical supplies and practice background. This work proposes a new method for relief demand calculation by dividing the process into two parts: supply classification and demand calculation. A general method for classifying relief supplies and clarifying the checklist of relief items for multi-disaster and multiple natural scenarios is given in detail, followed by the procedure of demand calculation for each relief material. The authors present a case study to validate the feasibility and effectiveness of the proposed method based on the disaster response practice in China. Detailed lists of relief demand for different types and severities of disaster are provided.


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