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• W4212819551 abstract "Natural disasters such as floods cause huge loss of life and property every year. Hence, it is imperative to detect and estimate the magnitude of a flood in a flood-affected area. Besides, it is essential to assess the damage caused by the flood as quickly as possible for an effective post-disaster relief and rescue effort. However, the longer frequency of data acquisition from the existing remote sensing-based methods for post-disaster damage assessment can delay relief. In this work, we propose an approach to estimate the magnitude of the flooded region by analyzing the aerial images acquired from unmanned aerial vehicles (UAV). The proposed method computes two parameters: one based on unsupervised image segmentation and another on image similarity between input and flooded images. These parameters are then utilized to develop a model to estimate the flood magnitude in the aerial image. The proposed approach is evaluated on the FloodNet dataset, and an Fl-score of 0.90 was obtained. demonstrating the proposed algorithm's robustness." @default.
• W4212819551 created "2022-02-24" @default.
• W4212819551 creator A5051946226 @default.
• W4212819551 creator A5079870633 @default.
• W4212819551 date "2021-12-07" @default.
• W4212819551 modified "2023-10-14" @default.
• W4212819551 title "Flood Magnitude Assessment from UAV Aerial Videos Based on Image Segmentation and Similarity" @default.
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• W4212819551 doi "https://doi.org/10.1109/tencon54134.2021.9707250" @default.
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