Rapid Earthquake Assessment from Satellite Imagery Using RPN and Yolo v3. Panday, S. P., Karn, S. L., Joshi, B., Shakya, A., & Pandey, R. K. In Huang, D., Jo, K., Li, J., Gribova, V., & Bevilacqua, V., editors, Proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021, volume 12836, of Intelligent Computing Theories and Application, Lecture Notes in Computer Science, pages 288–301, August, 2021. Springer, Cham. ![Rapid Earthquake Assessment from Satellite Imagery Using RPN and Yolo v3 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download Nepal suffers from earthquakes frequently as it lies in a highly earthquake prone region. The relief that is to be sent to the earthquake affected area requires rapid earliest assessment of the impact in the area. The number of damaged buildings provides us with the necessary information and can be used to assess the impact. Disaster damage assessment is one of the most important parts in providing information about the impact to the affected areas after the disaster. Rapid earthquake damage assessment can be done via the satellite imagery of the affected areas. This research work implements the Region Proposal Network (RPN) and You only look once (Yolo) v3 for generating region proposals and detection. Sliding window approach has been implemented for the method to work on large satellite imagery. The obtained detection has been com-pared with the ground truth. The proposed method achieved the overall F1 score of 0.89 as well as Precision of 0.94 and Recall of 0.86.
@inproceedings{panday2021rapid,
abstract = {Nepal suffers from earthquakes frequently as it lies in a highly earthquake prone region. The relief that is to be sent to the earthquake affected area requires rapid earliest assessment of the impact in the area. The number of damaged buildings provides us with the necessary information and can be used to assess the impact. Disaster damage assessment is one of the most important parts in providing information about the impact to the affected areas after the disaster. Rapid earthquake damage assessment can be done via the satellite imagery of the affected areas. This research work implements the Region Proposal Network (RPN) and You only look once (Yolo) v3 for generating region proposals and detection. Sliding window approach has been implemented for the method to work on large satellite imagery. The obtained detection has been com-pared with the ground truth. The proposed method achieved the overall F1 score of 0.89 as well as Precision of 0.94 and Recall of 0.86.},
added-at = {2023-03-07T12:13:40.000+0100},
author = {Panday, Sanjeeb Prasad and Karn, Saurav Lal and Joshi, Basanta and Shakya, Aman and Pandey, Rom Kant},
biburl = {https://www.bibsonomy.org/bibtex/2a2787623972771ef421c6f706e112003/amanshakya},
booktitle = {Proceedings of the 17th International Conference on Intelligent Computing, ICIC 2021},
doi = {10.1007/978-3-030-84522-3_23},
editor = {Huang, D.S. and Jo, K.H. and Li, J. and Gribova, V. and Bevilacqua, V.},
eventdate = {August 12--15, 2021},
eventtitle = {17th International Conference on Intelligent Computing, ICIC 2021},
interhash = {fb51cffd306b795cbdc4b8c80c4b2ca7},
intrahash = {a2787623972771ef421c6f706e112003},
keywords = {imported myown ugc},
month = {August},
pages = {288--301},
publisher = {Springer, Cham},
series = {Intelligent Computing Theories and Application, Lecture Notes in Computer Science},
timestamp = {2023-03-12T06:18:56.000+0100},
title = {Rapid Earthquake Assessment from Satellite Imagery Using RPN and Yolo v3},
url = {http://dx.doi.org/10.1007/978-3-030-84522-3_23},
venue = {Shenzhen, China},
volume = 12836,
year = 2021
}
Downloads: 1
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