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13 October 2022 Classifying buildings post hurricane based on transfer learning
Xinzhu Fu, Yanran Li, Yueyang Wu
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Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871N (2022) https://doi.org/10.1117/12.2640946
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China
Abstract
For disaster emergency response, higy-definition satellite imagery is quite important. However, what’s more important is machine learning and other advanced technologies which can use these images to get much more essential information. In this paper, we use transfer learning to classify buildings post hurricane. First, we propose a new model based on VGG-16, which can be more suitable to the experiment environment and get better performance. Second, we compare the efficiency and time the model use among different models in processing this task. We find that our model based on VGG-16 get the best accuracy which can reach to over 99%.
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Xinzhu Fu, Yanran Li, and Yueyang Wu "Classifying buildings post hurricane based on transfer learning", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871N (13 October 2022); https://doi.org/10.1117/12.2640946
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KEYWORDS
Performance modeling
Image classification
Data modeling
Satellite imaging
Machine learning
Visualization
Convolutional neural networks
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