A novel approach to urban flood monitoring using computer vision

2014 ◽  
Author(s):  
RamKumar Narayanan ◽  
V. M. Lekshmy ◽  
Sethuraman Rao ◽  
Kalyan Sasidhar
Keyword(s):  
Computer Vision ◽  
Urban Flood ◽  
Novel Approach ◽  
Flood Monitoring

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Flood monitoring using Sentinel-1 SAR images in Pearl River basin

2021 ◽  
Author(s):  
Qiu Junliang ◽  
Bowen Cao ◽  
Paolo Tarolli ◽  
Wenxin Zhang ◽  
Xiankun Yang
Keyword(s):  
River Basin ◽  
Pearl River ◽  
Flood Hazards ◽  
Pearl River Basin ◽  
Urban Flood ◽  
Flood Peak ◽  
Flood Monitoring ◽  
Land Cover Map ◽  
High Flood ◽  
The Pearl River

<p>The Pearl River Basin (PRB), as the second largest basin in China and one of the densely populated areas in China, is a critical region that exposes to high flood risks. Thus, it is indispensable to monitor the flooding patterns in PRB, so as to understand the flooding mechanism and better respond to the flood hazards. Previous studies about flood monitoring in PRB were mainly conducted by using gauging data of hydrological stations. However, the flood monitoring results would be prone to deviation in the region where the hydrological stations were sparse or without hydrological stations. Moreover, previous studies mainly focused on the urban flood in metropolis in PRB, neglecting the flood extents in rural area, in which the agriculture lands were constantly inundated by flooding water body. To monitor flood more comprehensively, this study will combine hydrological data, precipitation data with Sentinel-1 images to investigate spatial patterns of flood peak and flood extents in PRB. In addition, this study will also combine flood extents with land cover map to calculate the inundated areas of cropland during flood periods. This study will be valuable for flood mitigation, flood prevention and food guarantee in PRB.</p>

scholarly journals Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 825 ◽  
Author(s):  
Shih-Yen Hsu ◽  
Tai-Been Chen ◽  
Wei-Chang Du ◽  
Jyh-Horng Wu ◽  
Shih-Chieh Chen
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Weather Radar ◽  
Water Levels ◽  
Mitigation Measures ◽  
Extreme Weather Events ◽  
Television System ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Image Capturing

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

scholarly journals Image Segmentation Methods for Flood Monitoring System

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1825
Author(s):  
Nur Muhadi ◽  
Ahmad Abdullah ◽  
Siti Bejo ◽  
Muhammad Mahadi ◽  
Ana Mijic
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Digital Images ◽  
Rapid Development ◽  
Jaccard Index ◽  
Monitoring Systems ◽  
Dice Similarity Coefficient ◽  
Hybrid Technique ◽  
Segmentation Methods ◽  
Flood Monitoring

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

Progress of Urban Flood Research and Overall Handling of Urban Flood in China

2014 ◽  
Vol 955-959 ◽  
pp. 1881-1888 ◽  
Author(s):  
Zhe Yuan ◽  
Deng Hua Yan ◽  
Zhi Yong Yang ◽  
Jun Yin
Keyword(s):  
Urban Development ◽  
Early Warning ◽  
Climatic Changes ◽  
Future Research ◽  
Effective Solution ◽  
Urban Floods ◽  
Urban Flood ◽  
Flood Monitoring ◽  
Disaster Losses ◽  
Modern Technologies

Under the background of the climatic changes and the rapid urban development, the occurring frequency of urban floods grows increasingly, the influencing areas gradually spread, and the disaster losses become increasingly severe. The handling of urban flood has already become an issue requiring quick and effective solution during human social developing process. First, the causes of urban flood and the characteristics of disaster losses were analyzed under a changing environment. Then, Combined with the new progresses of relevant researches conducted at home and abroad, the key problems found in the research of urban flood was systematically studied. Moreover, it was pointed out that the urban flood monitoring, assessment, early warning forecast and handling based on modern technologies would become the highlights in the future research. On this basis, the paper summarized the problems existing in the flood handling of Chinese cities and discussed the overall handling frameworks.

scholarly journals Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5012 ◽  
Author(s):  
Bilal Arshad ◽  
Robert Ogie ◽  
Johan Barthelemy ◽  
Biswajeet Pradhan ◽  
Nicolas Verstaevel ◽  
...  
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Coastal Lagoons ◽  
Early Warning Systems ◽  
Flood Management ◽  
Sensor Data ◽  
Future Research ◽  
Warning Systems ◽  
Flood Monitoring ◽  
Computer Vision Applications

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

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