Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami
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[1] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[2] Qingjie Liu,et al. Road Extraction by Deep Residual U-Net , 2017, IEEE Geoscience and Remote Sensing Letters.
[3] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[4] Masashi Matsuoka,et al. Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images , 2001 .
[5] Shunichi Koshimura,et al. Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery , 2017 .
[6] A. Vetrivel,et al. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[7] Éric Gaussier,et al. A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.
[8] P. Gamba,et al. GIs and Image Understanding for Near-Real- Time Earthquake Damage Assessment , 1998 .
[9] Nazzareno Pierdicca,et al. Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake , 2018, Remote Sensing of Environment.
[10] Pierre Alliez,et al. Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[11] Takashi Nonaka,et al. Detection of damage to building side-walls in the 2011 Tohoku, Japan earthquake using high-resolution TerraSAR-X images , 2013, Remote Sensing.
[12] Shunichi Koshimura,et al. Mapping of Building Damage of the 2011 Tohoku Earthquake Tsunami in Miyagi Prefecture , 2012 .
[13] Ronald Kemker,et al. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.
[14] C. Schultz,et al. A medical disaster response to reduce immediate mortality after an earthquake. , 1996, The New England journal of medicine.
[15] Sameer Singh,et al. A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks , 2018, IEEE Geoscience and Remote Sensing Letters.
[16] Motoyuki Sato,et al. Urban Damage Level Mapping Based on Scattering Mechanism Investigation Using Fully Polarimetric SAR Data for the 3.11 East Japan Earthquake , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[17] Michael Kampffmeyer,et al. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[18] Amit Agarwal,et al. CNTK: Microsoft's Open-Source Deep-Learning Toolkit , 2016, KDD.
[19] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[20] Fumihiko Imamura,et al. Investigation of Tsunami-Induced Damage and Fragility of Buildings in Thailand after the December 2004 Indian Ocean Tsunami , 2006 .
[21] M. Crawford,et al. Earthquake damage identification using multi-temporal high-resolution optical satellite imagery , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..
[22] Ahsan Kareem,et al. Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[23] Evgeny Burnaev,et al. Satellite imagery analysis for operational damage assessment in Emergency situations , 2018, BIS.
[24] Nobuhito Mori,et al. Nationwide Post Event Survey and Analysis of the 2011 Tohoku Earthquake Tsunami , 2012 .
[25] Quoc Dung Cao,et al. Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery , 2018, ArXiv.
[26] Ryosuke Nakamura,et al. Damage detection from aerial images via convolutional neural networks , 2017, 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).
[27] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Fumihiko Imamura,et al. Damage Characteristic and Field Survey of the 2011 Great East Japan Tsunami in Miyagi Prefecture , 2012 .
[29] Fumio Yamazaki,et al. Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan , 2018 .
[30] Hamid Dehghani,et al. Using high-resolution satellite imagery to provide a relief priority map after earthquake , 2018, Natural Hazards.
[31] George Vosselman,et al. SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH , 2018 .
[32] André Stumpf,et al. Object-oriented mapping of landslides using Random Forests , 2011 .
[33] Shunichi Koshimura,et al. Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines , 2014 .