How computer vision can facilitate flood management: A systematic review

Iqbal, Umair; Perez, Pascal; Li, Wanqing; Barthélemy, Johan

doi:10.1016/j.ijdrr.2020.102030

Cited by 63 publications

(31 citation statements)

References 92 publications

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“…Cross-drainage structures (e.g., culverts, bridges) are prone to blockage by debris and are reported as one of the leading causes of flash floods in urban areas [1][2][3][4][5][6][7]. The 1998 and 2011 floods in Wollongong, Australia [1,[8][9][10][11] and the 2007 floods in Newcastle, Australia [1,12] are typical examples where blockage of cross drainage hydraulic structures contributed to triggering the flash flooding.…”

Section: Introductionmentioning

confidence: 99%

“…Detection of blockage (i.e., StopBlock) to avoid flash floods is one of the critical components of this project. From a management and maintenance perspective, making use of multi-dimensional information (i.e., visual blockage status, type of debris material, percentage of blocked openings) extracted using computer vision algorithms may prove helpful in making timely decisions, as suggested in literature [7,19]. As of now, to assess the visual blockage at culverts, manual visual inspections by flood management teams are performed to decide if a culvert needs maintenance towards avoiding the overtopping of flow and flash flooding.…”

Section: Introductionmentioning

confidence: 99%

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Automating Visual Blockage Classification of Culverts with Deep Learning

Iqbal

Barthélemy

et al. 2021

Applied Sciences

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Blockage of culverts by transported debris materials is reported as the salient contributor in originating urban flash floods. Conventional hydraulic modeling approaches had no success in addressing the problem primarily because of the unavailability of peak floods hydraulic data and the highly non-linear behavior of debris at the culvert. This article explores a new dimension to investigate the issue by proposing the use of intelligent video analytics (IVA) algorithms for extracting blockage related information. The presented research aims to automate the process of manual visual blockage classification of culverts from a maintenance perspective by remotely applying deep learning models. The potential of using existing convolutional neural network (CNN) algorithms (i.e., DarkNet53, DenseNet121, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3, NASNet) is investigated over a dataset from three different sources (i.e., images of culvert openings and blockage (ICOB), visual hydrology-lab dataset (VHD), synthetic images of culverts (SIC)) to predict the blockage in a given image. Models were evaluated based on their performance on the test dataset (i.e., accuracy, loss, precision, recall, F1 score, Jaccard Index, region of convergence (ROC) curve), floating point operations per second (FLOPs) and response times to process a single test instance. Furthermore, the performance of deep learning models was benchmarked against conventional machine learning algorithms (i.e., SVM, RF, xgboost). In addition, the idea of classifying deep visual features extracted by CNN models (i.e., ResNet50, MobileNet) using conventional machine learning approaches was also implemented in this article. From the results, NASNet was reported most efficient in classifying the blockage images with the 5-fold accuracy of 85%; however, MobileNet was recommended for the hardware implementation because of its improved response time with 5-fold accuracy comparable to NASNet (i.e., 78%). Comparable performance to standard CNN models was achieved for the case where deep visual features were classified using conventional machine learning approaches. False negative (FN) instances, false positive (FP) instances and CNN layers activation suggested that background noise and oversimplified labelling criteria were two contributing factors in the degraded performance of existing CNN algorithms. A framework for partial automation of the visual blockage classification process was proposed, given that none of the existing models was able to achieve high enough accuracy to completely automate the manual process. In addition, a detection-classification pipeline with higher blockage classification accuracy (i.e., 94%) has been proposed as a potential future direction for practical implementation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automating Visual Blockage Classification of Culverts with Deep Learning

Iqbal

Barthélemy

et al. 2021

Applied Sciences

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“…To address the challenges of natural events, the International Emergency Management System (IEMS) was established in 1993 to set up procedures and guidelines for countries to adapt during a crisis scenario. According to the Millenium Development Goals (MDG) 2015, the latest information technology and communication tools can be used for improving the relief response during a natural disaster [9][10][11][12]. The relief efforts can be accelerated to reach out to a maximum number of people in a short time with the use of advanced technology.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

et al. 2021

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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

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“…To reduce the damage caused by such flood events and provide effective disaster response and emergency plans, it is essential to quickly analyze the data collected from the disaster area [5]. There are many sources from which observational data can be collected.…”

Section: Introductionmentioning

confidence: 99%

“…The global popularity of low-cost sensors, especially vision sensors, as well as the rise of the Internet and the improvement of communication quality, has opened up the possibility of large-scale image monitoring. The utility of visual sensing has been increasingly recognized when using "cameras as sensors" to capture real-time information during and immediately after floods [5].…”

Section: Introductionmentioning

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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How computer vision can facilitate flood management: A systematic review

Cited by 63 publications

References 92 publications

Automating Visual Blockage Classification of Culverts with Deep Learning

Automating Visual Blockage Classification of Culverts with Deep Learning

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

Deep Sensing of Urban Waterlogging

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