Floodwater detection on roadways from crowdsourced images

Witherow, Megan A.; Sazara, Cem; Winter-Arboleda, Irina M.; Elbakary, M. I.; Cetin, Mecit; Iftekharuddin, Khan M.

doi:10.1080/21681163.2018.1488223

Cited by 24 publications

(12 citation statements)

References 10 publications

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“…The challenge is how to extract usable information from the data, particularly when using social media‐derived data that do not follow a consistent format. Witherow, Elbakary, Iftekharuddin, and Cetin (2018) and Witherow et al (2018) proposed a method for extracting street inundation information from crowdsourced images taken at near ground level in the city of Norfolk, Virginia, but found this challenging due to the lack of a fixed camera with a known location. Smith, Liang, James, and Lin (2017) present a real‐time modeling framework in Newcastle upon Tyne, the United Kingdom, to identify areas likely to have flooded using data obtained through social media (Twitter).…”

Section: Monitoring Surface Water Flood Impacts In Real Timementioning

confidence: 99%

Operational and emerging capabilities for surface water flood forecasting

et al. 2021

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Surface water (or pluvial) flooding is caused by intense rainfall before it enters rivers or drainage systems. As the climate changes and urban populations grow, the number of people around the world at risk of surface water flooding is increasing. Although it may not be possible to prevent such flooding, reliable and timely flood forecasts can help improve preparedness and recovery. Unlike riverine and coastal flooding where forecasting methods are well established, surface water flood forecasting presents a unique challenge due to the high uncertainties around predicting the location, timing, and impact of what are typically localized events. Over the past 5 years, there has been rapid development of convection‐permitting numerical weather prediction models, ensemble forecasting, and computational ability. It is now theoretically feasible to develop operational surface water forecasting systems. This paper identifies three approaches to surface water forecasting utilizing state‐of‐the‐art meteorological forecasts: empirical‐based scenarios, hydrological forecasts linked to presimulated impact scenarios, and real‐time hydrodynamic simulation. Reviewing operational examples of each approach provides an opportunity to learn from international best practice to develop targeted, impact‐based, surface water forecasts to support informed decision‐making. Although the emergence of new meteorological and hydrological forecasting capabilities is promising, there remains a scientific limit to the predictability of convective rainfall. To overcome this challenge, we suggest that a rethink of the established role of flood forecasting is needed, alongside the development of interdisciplinary solutions for communicating uncertainty and making the best use of all available data to increase preparedness. This article is categorized under: Engineering Water > Engineering Water

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Section: Monitoring Surface Water Flood Impacts In Real Timementioning

confidence: 99%

Operational and emerging capabilities for surface water flood forecasting

et al. 2021

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“…The convenience and versatility of smartphones have resulted in the in situ information collected from crowdsourced or crawled from social media (secondary sources) being frequently used as input data for sensing floods [32,33].…”

Section: A Sensing From Crowdsourced and Social Media Imagesmentioning

confidence: 99%

Deep Sensing of Urban Waterlogging

Chang

et al. 2021

IEEE Access

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In the monsoon season, sudden flood events occur frequently in urban areas, which hamper the social and economic activities and may threaten the infrastructure and lives. The use of an efficient largescale waterlogging sensing and information system can provide valuable near real-time disaster information to facilitate disaster management and enhance awareness of the general public to alleviate losses during and after flood disasters. Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging sensing and event-location mapping. The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale. The system could sense approximately 2379 vision sources through an internet of video things framework and transmit the event-location information in 5 min. The proposed approach can sense waterlogging events at a national scale and provide an efficient and highly scalable alternative to conventional waterlogging sensing methods.INDEX TERMS deep neural network, internet of video things, urban flood, urban waterlogging, visual sensing

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“…Witherow et al proposed an image processing pipeline for detecting the floodwater extent (i.e., for identifying image pixels corresponding to flooded areas) on georeferenced photos depicting inundated roadways, from image data captured by smartphones [33]. The proposed method is based on aligning and comparing pairs of images depicting dry versus flooded scenes, over the same location.…”

Section: Related Workmentioning

confidence: 99%

“…This new data collection methodology can have the advantage of providing a local perspective on inundations, whereas previous studies mostly relied on remotely sensed data from an overhead perspective [2,3,14,23,25,26,29,34]. The local detail of crowdsourced images can perhaps provide useful information for estimating the boundaries of flood-water, including partial blockage of roadways due to flooding [4,5,24,33], which is important and otherwise very hard or impossible to achieve with conventional sensors. Crowdsourced georeferenced images can also be used to complement other information in flood monitoring systems (e.g., platforms such as the European Flood Awareness System 1 ), through Geographic Information Retrieval (GIR) methods for selecting relevant and representative images in connection to particular flooding events [22].…”

Section: Introductionmentioning

confidence: 99%

Assessing flood severity from georeferenced photos

Pereira

Monteiro

Estima

et al. 2019

Proceedings of the 13th Workshop on Geographic Information Retrieval

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The use of georeferenced social media data in disaster and crisis management is increasing rapidly. Particularly in connection to flooding events, georeferenced images shared by citizens can provide situational awareness to emergency responders, as well as assistance to financial loss assessment, giving information that would otherwise be very hard to collect through conventional sensors or remote sensing products. Moreover, recent advances in computer vision and deep learning can perhaps support the automated analysis of these data. In this paper, focusing on ground-level images taken by humans during flooding events, we evaluate the use of deep convolutional neural networks for (i) discriminating images showing direct evidence of a flood, and (ii) estimating the severity of the flooding event. Considering distinct datasets (i.e., the European Flood 2013 dataset, and data from different editions of the MediaEval Multimedia Satellite Task), we specifically evaluated models based on the DenseNet and EfficientNet neural network architectures, concluding that these models for image classification can achieve a very high accuracy on both tasks. CCS CONCEPTS • Computing methodologies → Computer vision tasks; Neural networks; Visual content-based indexing and retrieval; Supervised learning.

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Floodwater detection on roadways from crowdsourced images

Cited by 24 publications

References 10 publications

Operational and emerging capabilities for surface water flood forecasting

Operational and emerging capabilities for surface water flood forecasting

Deep Sensing of Urban Waterlogging

Assessing flood severity from georeferenced photos

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