TY - GEN
T1 - Collapsed Building Detection Using Residual Siamese Neural Network on LiDAR Data
AU - Ramadhan, Mgs M.Luthfi
AU - Jati, Grafika
AU - Alhamidi, Machmud Roby
AU - Riskyana Dewi Intan, P.
AU - Hilman, Muhammad Hafizhuddin
AU - Jatmiko, Wisnu
N1 - Funding Information:
ACKNOWLEDGMENT We would like to thank Asia Air Survey Co. Ltd for providing the pre-earthquake and post-earthquake LiDAR data. This work was also supported by Indonesia Ministry of Research and Technology/National Research and Innovation Agency 2021, No: NKB-232/UN2.RST/HKP.05.00/2021.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Evaluation of buildings is crucial to aid emergency response but it costs a lot of resources to do it manually. Many approaches have been proposed to automate the process using artificial intelligence. Most of them, use handcrafted feature, difference calculation between pre-disaster and post-disaster feature, and a classifier model separately. In this study, the process from feature extraction, feature difference and classification are represented by a single model which is siamese neural network. Furthermore, we modify siamese neural network by implementing residual connection for feature concatenation purposes. We evaluate our model on Kumamoto Prefecture earthquake LiDAR data. The result shows the modified model is able to outperform the baseline model with Accuracy and F-measure of 90.91% and 79.28% respectively.
AB - Evaluation of buildings is crucial to aid emergency response but it costs a lot of resources to do it manually. Many approaches have been proposed to automate the process using artificial intelligence. Most of them, use handcrafted feature, difference calculation between pre-disaster and post-disaster feature, and a classifier model separately. In this study, the process from feature extraction, feature difference and classification are represented by a single model which is siamese neural network. Furthermore, we modify siamese neural network by implementing residual connection for feature concatenation purposes. We evaluate our model on Kumamoto Prefecture earthquake LiDAR data. The result shows the modified model is able to outperform the baseline model with Accuracy and F-measure of 90.91% and 79.28% respectively.
KW - collapsed building assessment
KW - deep learning
KW - earthquake
KW - siamese neural network
UR - http://www.scopus.com/inward/record.url?scp=85124366553&partnerID=8YFLogxK
U2 - 10.1109/IWBIS53353.2021.9631844
DO - 10.1109/IWBIS53353.2021.9631844
M3 - Conference contribution
AN - SCOPUS:85124366553
T3 - Proceedings - IWBIS 2021: 6th International Workshop on Big Data and Information Security
SP - 29
EP - 34
BT - Proceedings - IWBIS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Workshop on Big Data and Information Security, IWBIS 2021
Y2 - 23 October 2021 through 26 October 2021
ER -