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.
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Panday, S.P., Karn, S.L., Joshi, B., Shakya, A., Pandey, R.K. (2021). Rapid Earthquake Assessment from Satellite Imagery Using RPN and Yolo v3. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_23
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