FloodIMG: Flood image DataBase system

Floods are on the rise globally with the frequent record-breaking events occurring during the past few years in the US alone. These extreme events pose a considerable threat to human life and result in destructive damage to property, communities, and the built environment (e.g., Phillips et al., 2018). The south and the southeast US have experienced frequent storms with annually, on average, more than 85 named and unnamed thunderstorms (NWS, 2020). These events happened in quick succession (∼2 weeks apart) and produced catastrophic flooding in wide geographic areas (∼1,000 km swath) and within short timespans (less than a 48-hr period; Donratanapat et al., 2020). Successive flood events can even lead to higher costs in terms of repairing and rebuilding destroyed buildings and critical infrastructures (CIs) due to a lack of early warning systems (e.g., Donratanapat et al., 2020;Field et al., 2012;Hinkel et al., 2014). This necessitates the importance of detecting flood magnitudes ahead of the event to protect communities and CIs. The flood stage is the height of the water surface in a stream gaging station, not the height throughout the stream. A vast amount of research has been conducted to develop different tools and test their reliability in predicting near real-time flood stage estimation (Krzysztofowicz et al., 1994).

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An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

Windheuser

Karanjit

Phillips

et al. 2023

Earth and Space Science

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WaRENet: A Novel Urban Waterlogging Risk Evaluation Network

Yu,

Wu,

Yang

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

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In this paper, we propose a novel urban waterlogging risk evaluation network (WaRENet) to evaluate the risk of waterlogging. The WaRENet distinguishes whether an urban image involves waterlogging by classification module, and estimates the waterlogging risk levels by multi-class reference objects detection module (MCROD). Firstly, in the waterlogging scene classification, ResNet combined with Se-block is used to identify the waterlogging scene, and lightweight gradient-weighted class activation mapping (Grad-CAM) is also integrated to roughly locate overall waterlogging areas with low computational burden. Secondly, in the MCROD module, we detect reference objects, e.g., cars and persons in waterlogging scenes. The positional relationship between water depths and reference objects serves as risk indicators for accurately evaluating waterlogging risk. Specifically, we incorporate switchable atrous convolution (SAC) into YOLOv5 to solve occlusions and varying scales problems in complex waterlogging scenes. Moreover, we construct a large-scale urban waterlogging dataset named UrbanWaterloggingRiskDataset (UWRDataset) with 6351 images for waterlogging scene classification and 3217 images for reference objects detection. Experimental results on the dataset show that our WaRENet outperforms all comparison methods. The waterlogging scene classification module achieves accuracy of 95.99 \(\% \) . The MCROD module obtains mAP of 54.9 \(\% \) , while maintaining a high processing speed of 70.04 fps.

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FloodIMG: Flood image DataBase system

Cited by 2 publications

References 9 publications

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

WaRENet: A Novel Urban Waterlogging Risk Evaluation Network

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