Real-time water level monitoring using live cameras and computer vision techniques

Jafari, Navid H.; Li, Xin; Chen, Qin; Le, Canyu; Betzer, Logan P.; Liang, Yongqing

doi:10.1016/j.cageo.2020.104642

Cited by 45 publications

(23 citation statements)

References 16 publications

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“…More recently, Jafari et al [42] proposed a real-time water level monitoring approach using images from live cameras. They applied a deep learning-based semantic segmentation algorithm to label the water body and reference objects (staff gauge, pier) as a scale.…”

Section: B Sensing From In Situ Video Streamingmentioning

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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Section: B Sensing From In Situ Video Streamingmentioning

confidence: 99%

Deep Sensing of Urban Waterlogging

Chang

et al. 2021

IEEE Access

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“…It defines segments rather than pixels to classify areas, and it incorporates meaningful spectral and non-spectral features for class separation, thereby providing a clear illustration of landscape patterns [43][44][45][46]. Owing to its superiority and efficiency [47], OBIA has been utilized in many different areas, such as computer vision [48,49], biomedical imaging [50,51], and environmental scanning electron microscopy (SEM) analysis [52][53][54]. Just as remote sensing images are numeric representations of the earth surface landscape consisting of water area, forest land, wetlands, etc.…”

Section: Introductionmentioning

confidence: 99%

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

Zhao

Yan

Jin

et al. 2022

Forests

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In the context of rapid urbanization, urban foresters are actively seeking management monitoring programs that address the challenges of urban biodiversity loss. Passive acoustic monitoring (PAM) has attracted attention because it allows for the collection of data passively, objectively, and continuously across large areas and for extended periods. However, it continues to be a difficult subject due to the massive amount of information that audio recordings contain. Most existing automated analysis methods have limitations in their application in urban areas, with unclear ecological relevance and efficacy. To better support urban forest biodiversity monitoring, we present a novel methodology for automatically extracting bird vocalizations from spectrograms of field audio recordings, integrating object-based classification. We applied this approach to acoustic data from an urban forest in Beijing and achieved an accuracy of 93.55% (±4.78%) in vocalization recognition while requiring less than ⅛ of the time needed for traditional inspection. The difference in efficiency would become more significant as the data size increases because object-based classification allows for batch processing of spectrograms. Using the extracted vocalizations, a series of acoustic and morphological features of bird-vocalization syllables (syllable feature metrics, SFMs) could be calculated to better quantify acoustic events and describe the soundscape. A significant correlation between the SFMs and biodiversity indices was found, with 57% of the variance in species richness, 41% in Shannon’s diversity index and 38% in Simpson’s diversity index being explained by SFMs. Therefore, our proposed method provides an effective complementary tool to existing automated methods for long-term urban forest biodiversity monitoring and conservation.

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“…In fact, this approach is usable only when the gauge is highly distinguishable from the background, which is not always a true hypothesis. Jafari et al [25] propose an advanced method which exploits CNN and leverages time-lapse photos and object-based image analysis. The methodology reaches very good performances in both the laboratory and two field experiments, nonetheless it relies on a strong site-specific adaptation phase.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision System for Staff Gauge in River Flood Monitoring

et al. 2021

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Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

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Real-time water level monitoring using live cameras and computer vision techniques

Cited by 45 publications

References 16 publications

Deep Sensing of Urban Waterlogging

Deep Sensing of Urban Waterlogging

Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

A Computer Vision System for Staff Gauge in River Flood Monitoring

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