Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
http://link.springer.com/content/pdf/10.1007/s00371-020-02043-9.pdf
Reference36 articles.
1. Dell’Acqua, F., Gamba, P.: Remote sensing and earthquake damage assessment: experiences, limits, and perspectives. Proc. IEEE 100, 2876–2890 (2012)
2. Eguchi, R.T., Huyck, C.K., Ghosh, S., Adams, B.J., McMillan, A.: Utilizing new technologies in managing hazards and disasters. In: Showalter, P., Lu, Y. (eds.) Geospatial Techniques in Urban Hazard and Disaster Analysis, pp. 295–323. Springer, Dordrecht (2009)
3. Schweier, C., Markus, M.: Classification of collapsed buildings for fast damage and loss assessment. Bull. Earthq. Eng. 4, 177–192 (2006)
4. Rupnik, E., Nex, F., Toschi, I., Remondino, F.: Contextual classification using photometry and elevation data for damage detection after an earthquake event. Eur. J. Remote Sens. 51, 543–557 (2018)
5. Dubois, D., Lepage, R.: Fast and efficient evaluation of building damage from very high resolution optical satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 4167–4176 (2014)
Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Towards improving synthetic-to-real image correlation for instance recognition in structure monitoring;The Visual Computer;2024-03-19
2. Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images;Applied Sciences;2024-02-04
3. Deep Learning Models for Hazard-Damaged Building Detection Using Remote Sensing Datasets: A Comprehensive Review;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
4. Aerial imagery segmentation of natural disaster-affected areas using deep convolutional networks for disaster assessment;AIP Conference Proceedings;2024
5. Aerial Imagery of Natural Disaster-Affected Areas (AINDAA) Dataset for Semantic Segmentation and Natural Disaster Assessment;2023 IEEE 7th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE);2023-11-29
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3