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Disaster events occur around the world and cause significant damage to human life and property.
Yonezawa, C. and Takeuchi, S.: Decorrelation of SAR data by urban damages caused by the 1995 Hyogoken-nanbu earthquake, Int. J. Remote Sens., 22, 1585–1600, 2001
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Yusuf, Y., Matsuoka, M., and Yamazaki, F.: Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images, Journal of the Indian Society of Remote Sensing, 29, 17–22, 2001
2001
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Matsuoka, M. and Yamazaki, F.: Building Damage Mapping of the 2003 Bam, Iran, Earthquake Using Envisat/ASAR Intensity Imagery, Earthquake Spectra, 21, 285–294, 2005
2005
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Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., and Tertulliani, A.: Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies, Int. J. Remote Sens., 27, 4433–4447, 2006
2006
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Arciniegas, G. A., Bijker, W., Kerle, N., and Tolpekin, V. A.: Coherence- and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data, IEEE T. Geosci. Remote, 45, 1571–1581, 2007
2007
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Yamazaki, F. and Matsuoka, M.: Remote Sensing Technologies in Post-Disaster Damage Assessment, Journal of Earthquake and Tsunami, 01, 193–210, 2007
2007
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van der Maaten, L. and Hinton, G.: Visualizing Data using t-SNE, J. Mach. Learn. Res., 9, 2579–2605, 2008
2008
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Chini, M., Pierdicca, N., and Emery, W. J.: Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake, IEEE T. Geosci. Remote, 47, 145–152, 2009
2009
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Brunner, D., Lemoine, G., and Bruzzone, L.: Earthquake damage assessment of buildings using VHR optical and SAR imagery, IEEE T. Geosci. Remote, 48, 2403–2420, 2010
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Matsuoka, M. and Yamazaki, F.: Comparative analysis for detecting areas with building damage from several destructive earthquakes using satellite synthetic aperture radar images, Journal of Applied Remote Sensing, 4, 041 867, 2010
2010
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Matsuoka, M., Koshimura, S., and Nojima, N.: Estimation of building damage ratio due to earthquakes and tsunamis using satellite SAR imagery, in: Int. Geosci. Remote Se., pp. 3347–3349, 2010
2010
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Tong, X., Hong, Z., Liu, S., Zhang, X., Xie, H., Li, Z., Yang, S., Wang, W., and Bao, F.: Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake, ISPRS J. Photogramm., 68, 13–27, 2012
2012
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Wang, T.-L. and Jin, Y.-Q.: Postearthquake Building Damage Assessment Using Multi-Mutual Information From Pre-Event Optical Image and Postevent SAR Image, IEEE Geosci. Remote S., 9, 452–456, 2012
2012
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Yamaguchi, Y.: Disaster Monitoring by Fully Polarimetric SAR Data Acquired With ALOS-PALSAR, P. IEEE, 100, 2851–2860, 2012
2012
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Brett, P. T. B. and Guida, R.: Earthquake Damage Detection in Urban Areas Using Curvilinear Features, IEEE T. Geosci. Remote, 51, 4877–4884, 2013
2013
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Chen, S.-W. and Sato, M.: Tsunami Damage Investigation of Built-Up Areas Using Multitemporal Spaceborne Full Polarimetric SAR Images, IEEE T. Geosci. Remote, 51, 1985–1997, 2013
2013
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Liu, W., Yamazaki, F., Gokon, H., and ichi Koshimura, S.: Extraction of Tsunami-Flooded Areas and Damaged Buildings in the 2011 Tohoku-Oki Earthquake from TerraSAR-X Intensity Images, Earthquake Spectra, 29, 183–200, 2013
2013
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Freire, S., Santos, T., Navarro, A., Soares, F., Silva, J., Afonso, N., Fonseca, A., and Tenedório, J.: Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery, ISPRS J. Photogramm., 90, 1–9, 2014
2014
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Chini, M., Anniballe, R., Bignami, C., Pierdicca, N., Mori, S., and Stramondo, S.: Identification of building double-bounces feature in very high resoultion SAR data for earthquake damage mapping, in: Int. Geosci. Remote Se., pp. 2723–2726, 2015
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Prendes, J., Chabert, M., Pascal, F., Giros, A., and Tourneret, J.-Y.: Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field, in: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1513–1517, 2015
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Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241, 2015
2015
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Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c.: Convolutional LSTM Network: a machine learning approach for precipitation nowcasting, in: Adv. Neur. In., vol. 1, p. 802–810, 2015
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Watanabe, M., Thapa, R. B., Ohsumi, T., Fujiwara, H., Yonezawa, C., Tomii, N., and Suzuki, S.: Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2, Earth, Planets and Space, 68, 131, 2016
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Xie, S., Duan, J., Liu, S., Dai, Q., Liu, W., Ma, Y., Guo, R., and Ma, C.: Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake, Remote Sensing, 8, 2016
2016
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Zhang, P., Gong, M., Su, L., Liu, J., and Li, Z.: Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images, ISPRS J. Photogramm., 116, 24–41, 2016
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Bai, Y., Adriano, B., Mas, E., and Koshimura, S.: Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake, Journal of Disaster Research, 12, 646–655, 2017
2017
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Fujita, A., Sakurada, K., Imaizumi, T., Ito, R., Hikosaka, S., and Nakamura, R.: Damage detection from aerial images via convolutional neural networks, in: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 5–8, 2017
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Karimzadeh, S. and Mastuoka, M.: Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy, Remote Sensing, 9, 2017
2017
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Liu, W. and Yamazaki, F.: Extraction of Collapsed Buildings in the 2016 Kumamoto Earthquake Using Multi-Temporal PALSAR-2 Data, Journal of Disaster Research, 12, 241–250, 10.20965/jdr.2017.p0241 , 2017
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Nguyen, D. T., Ofli, F., Imran, M., and Mitra, P.: Damage Assessment from Social Media Imagery Data During Disasters, in: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, p. 569–576, 2017
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Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D.: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, in: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626, 2017
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Shaban, A., Bansal, S., Liu, Z., Essa, I., and Boots, B.: One-Shot Learning for Semantic Segmentation, in: Proceedings of the British Machine Vision Conference (BMVC), pp. 167.1–167.13, 2017
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Tarvainen, A. and Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: Adv. Neur. In., edited by Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., vol. 30, 2017
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Ye, Y., Shen, L., Hao, M., Wang, J., and Xu, Z.: Robust Optical-to-SAR Image Matching Based on Shape Properties, IEEE Geosci. Remote S., 14, 564–568, 2017
2017
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Bai, Y., Gao, C., Singh, S., Koch, M., Adriano, B., Mas, E., and Koshimura, S.: A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks, IEEE Geosci. Remote S., 15, 43–47, 2018
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Berman, M., Triki, A. R., and Blaschko, M. B.: The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks, in: Proc. CVPR IEEE, 2018
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Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, in: Computer Vision – ECCV 2018, pp. 833–851, 2018
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Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M.: Learning to Adapt Structured Output Space for Semantic Segmentation, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7472–7481, 2018
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Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S.: Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia, Remote Sensing, 11, 2019
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Artés, T., Oom, D., de Rigo, D., Durrant, T. H., Maianti, P., Libertà, G., and San-Miguel-Ayanz, J.: A global wildfire dataset for the analysis of fire regimes and fire behaviour, Scientific Data, 6, 296, 2019
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Fan, X., Nie, G., Deng, Y., An, J., Zhou, J., Xia, C., and Pang, X.: Estimating earthquake-damage areas using Landsat-8 OLI surface reflectance data, International Journal of Disaster Risk Reduction, 33, 275–283, 2019
2019
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Ge, P., Gokon, H., Meguro, K., and Koshimura, S.: Study on the Intensity and Coherence Information of High-Resolution ALOS-2 SAR Images for Rapid Massive Landslide Mapping at a Pixel Level, Remote Sensing, 11, 2019
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Gupta, R., Goodman, B., Patel, N., Hosfelt, R., Sajeev, S., Heim, E., Doshi, J., Lucas, K., Choset, H., and Gaston, M.: Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery, in: IEEE Comput. Soc. Conf., 2019
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Rahnemoonfar, M., Chowdhury, T., and Murphy, R.: RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment, Scientific Data, 10, 913, 10.1038/s41597-023-02799-4 , 2023
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Wang, D., Zhang, Q., Xu, Y., Zhang, J., Du, B., Tao, D., and Zhang, L.: Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model, IEEE T. Geosci. Remote, 61, 1–15, 2023
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Xia, H., Wu, J., Yao, J., Zhu, H., Gong, A., Yang, J., Hu, L., and Mo, F.: A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake, Int. J. Disast. Risk Sc., 14, 947–962, 2023
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Lian, Q., Duan, L., Lv, F., and Gong, B.: Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach, in: IEEE I. Conc. Comp. Vis., pp. 6757–6766, 2019
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Ye, Y., Bruzzone, L., Shan, J., Bovolo, F., and Zhu, Q.: Fast and Robust Matching for Multimodal Remote Sensing Image Registration, IEEE T. Geosci. Remote, 57, 9059–9070, 2019
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Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E.: Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1, in: IEEE Comput. Soc. Conf., 2020
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Chen, H., Wu, C., Du, B., Zhang, L., and Wang, L.: Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network, IEEE T. Geosci. Remote, 58, 2848–2864, 2020
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Ge, P., Gokon, H., and Meguro, K.: A review on synthetic aperture radar-based building damage assessment in disasters, Remote Sens. Environ., 240, 111 693, 2020
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Ke, Z., Qiu, D., Li, K., Yan, Q., and Lau, R. W. H.: Guided Collaborative Training for Pixel-Wise Semi-Supervised Learning, in: European Conference Computer Vision, p. 429–445, 2020
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