V-FloodNet: A video segmentation system for urban flood detection and quantification

Liang, Yongqing; Li, Xin; Tsai, Brian; Chen, Qin; Jafari, Navid H.

doi:10.1016/j.envsoft.2022.105586

Cited by 17 publications

(5 citation statements)

References 21 publications

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“…The objective of this study is to develop an image database system that can be used to train deep-learning algorithms for flood emergency management and risk assessment. Several recent types of research (for example: [ 6 , 7 ]) are being conducted on water and images, but very few are related to flooding events. [ 6 , 7 ] contain data on general water bodies but do not specifically include flooding images.…”

Section: Objectivementioning

confidence: 99%

“…Several recent types of research (for example: [ 6 , 7 ]) are being conducted on water and images, but very few are related to flooding events. [ 6 , 7 ] contain data on general water bodies but do not specifically include flooding images. Other recent research uses already available datasets like the MS COCO dataset [8] , which does not contain event-specific images.…”

Section: Objectivementioning

confidence: 99%

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FloodIMG: Flood image DataBase system

2023

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Section: Objectivementioning

confidence: 99%

Section: Objectivementioning

confidence: 99%

FloodIMG: Flood image DataBase system

2023

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“…The outputs from one layer are then passed as inputs to the next layer, and this process is repeated until the nal output is produced. The ability of DNNs to learn complex relationships and make predictions with high accuracy has made them a popular choice for a wide range of applications [9] [10] [11]. Furthermore, DNNs are utilized for ood forecasting using meteorological data from different gauge stations over watersheds prone to ooding [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Hawamdeh,

Tarawneh,

Sharrab

et al. 2024

Preprint

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Natural disasters can be devastating to the environment and natural resources. Flood inundation mapping and hydraulic modeling are essential to forecast critical flood information, including flood depth and water surface height. In this research, several factors that influence floods were studied. These factors include the intensity of the rainstorm, the depth of precipitation, soil types, geologic settings, and topographic features. Furthermore, the research carried out hydraulic modeling of storm flows for 50- and 100-Year return periods and estimated that the water depth in Wadi Al Wala could reach 15m at 50 years of storm and 25m at 100 return years of storms. A DNN model is developed with good accuracy to predict flood flow based on historical records from 1980 to 2018 meteorological data. The goal of this research is to improve flood prediction, and risk assessment with the use of DNN integrated with hydrological and hydraulic models.

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“…Lastly, ref. [11] combined video and segmentation technologies to estimate water levels from images and use the objects identified within images to provide spatial scale references.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates

Tedesco,

Radzikowski

2023

GeoHazards

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Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R2 of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1).

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V-FloodNet: A video segmentation system for urban flood detection and quantification

Cited by 17 publications

References 21 publications

FloodIMG: Flood image DataBase system

FloodIMG: Flood image DataBase system

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates

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