Identification of undamaged buildings after the event of disaster using Deep Learning
Pages 27 - 31
Abstract
As direct response, or recovery and security operations, it is of paramount importance to establish precisely the location and assess the extent of damage to a building as quickly as possible after a tragic event. The automation of damage analysis may enhance the capability of administration to provide the help. For the same, convolutional neural-networks are being used by recent proposals to perform image classification of building damage depending on the amount and type of damage to be detected. Furthermore, the use of up/down-sampling images during CNN preparation helps in better damage recognition. However, a number of challenges has been observed in convolutional neural-networks-based methods such as multi-resolution images of damaged areas. Furthermore, recent convolutional neural-networks-based models are having very complex architecture which increases the requirement of computational power. Therefore, in this paper, a simple convolutional neural-network model has been presented which effectively identifies the damage and undamaged buildings after the natural disaster. The presented method has been compared with recent convolutional neural-network models. The experimental results shows that the simple convolutional neural-network outperforms the existing models with a 99.2% validation accuracy.
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Index Terms
- Identification of undamaged buildings after the event of disaster using Deep Learning
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Published In
August 2022
710 pages
ISBN:9781450396752
DOI:10.1145/3549206
- General Chairs:
- Sartaj Sahni,
- Vikas Saxena,
- Program Chair:
- Sundaraja Sitharama Iyengar
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Published: 24 October 2022
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IC3-2022
IC3-2022: 2022 Fourteenth International Conference on Contemporary Computing
August 4 - 6, 2022
Noida, India
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