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Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

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Figure

Figure 1. As an alternative to conventional sensorsfractional water-covered area (SOFI) over a series of video frames (a), flood trend information is extracted from surveillance footage by computing the (b).
Figure 3. Public-domain images of flooding, representative of the dataset provided by Chaudhary (2018) that was used in this work.
Table 2. Surveillance footage used in study.
Figure 5. Automatic segmentation performance measured in terms of the intersection over union (IoU) for each video using the Basic (B),Augmented (A), and Fine-tuned (F) DCNNs
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