Extraction of regions with similar temporal evolution using Earth Observation Big Data. Application to water turbidity dynamics

Cucu-Dumitrescu, Catalin; Constantin, Sorin

doi:10.1080/2150704x.2017.1312024

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Unexpected variations of the water turbidity occurred in relatively short times (e.g., hours) and they persisted for several days, as in many other coastal areas 43 . Turbid waters and changes in light diffusion reduced the camera’s field of view.…”

Section: Discussionmentioning

confidence: 83%

Tracking Fish Abundance by Underwater Image Recognition

Marini

Fanelli

Sbragaglia

et al. 2018

Sci Rep

129

101

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Marine cabled video-observatories allow the non-destructive sampling of species at frequencies and durations that have never been attained before. Nevertheless, the lack of appropriate methods to automatically process video imagery limits this technology for the purposes of ecosystem monitoring. Automation is a prerequisite to deal with the huge quantities of video footage captured by cameras, which can then transform these devices into true autonomous sensors. In this study, we have developed a novel methodology that is based on genetic programming for content-based image analysis. Our aim was to capture the temporal dynamics of fish abundance. We processed more than 20,000 images that were acquired in a challenging real-world coastal scenario at the OBSEA-EMSO testing-site. The images were collected at 30-min. frequency, continuously for two years, over day and night. The highly variable environmental conditions allowed us to test the effectiveness of our approach under changing light radiation, water turbidity, background confusion, and bio-fouling growth on the camera housing. The automated recognition results were highly correlated with the manual counts and they were highly reliable when used to track fish variations at different hourly, daily, and monthly time scales. In addition, our methodology could be easily transferred to other cabled video-observatories.

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Section: Discussionmentioning

confidence: 83%

Tracking Fish Abundance by Underwater Image Recognition

Marini

Fanelli

Sbragaglia

et al. 2018

Sci Rep

129

101

View full text Add to dashboard Cite

show abstract

“…In the article, the authors considered the optimal deployment problem (ODP) based on the local memory of each viewer. In [13], the authors developed an innovative method of extracting valuable pixel categories with similar evolution for specific parameters of interest over a long period of time to obtain valuable comprehensive information. Unsupervised classification was performed using an original custom method suitable for execution on such an enormous data set.…”

Section: Introductionmentioning

confidence: 99%

Intelligent cloud workflow management and scheduling method for big data applications

Hu¹,

Wang

2020

J Cloud Comp

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With the application and comprehensive development of big data technology, the need for effective research on cloud workflow management and scheduling is becoming increasingly urgent. However, there are currently suitable methods for effective analysis. To determine how to effectively manage and schedule smart cloud workflows, this article studies big data from various aspects and draws the following conclusions: Compared with the original JStorm system, the response time is shortened by a maximum of 58.26% and an average of 23.18%, CPU resource utilization is increased by a maximum of 17.96% and an average of 11.39%, and memory utilization increased by a maximum of 88.7% and an average of 71.16%. In terms of optimizing the dynamic combination of web services, the overall performance of both the MOACO and CCA algorithms is better than that of the GA algorithm, and the average performance of the MOACO algorithm is better than that of the CCA algorithm. This paper also proposes a cloud workflow scheduling strategy based on an intelligent algorithm and realizes the twotier scheduling of cloud workflow tasks by adjusting the combination strategy for cloud service resources. We have studied three representative intelligent algorithms (ACO, PSO and GA) and improved them for scheduling optimization. It can be clearly seen that in the same scenario, the optimal values of the different algorithms vary greatly for different test cases. However, the optimal solution curve is substantially consistent with the trend of the mean curve.

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“…Considering the NP difficulty of calculating the optimal solution, the author turns to a computationally evaluable algorithm. In [8], the authors developed an innovative method to extract valuable pixel categories with similar evolution for specific parameters of interest over a long period of time to obtain valuable comprehensive information. The algorithm has been applied to the time series of satellite turbidity products in the coastal areas of the Danube Delta, with a total of approximately 5,700 independent scenarios.…”

mentioning

confidence: 99%

Intelligent Cloud Workflow Management and Scheduling Method for Big Data Applications

Wang

2019

Preprint

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With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.

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Extraction of regions with similar temporal evolution using Earth Observation Big Data. Application to water turbidity dynamics

Cited by 5 publications

References 11 publications

Tracking Fish Abundance by Underwater Image Recognition

Tracking Fish Abundance by Underwater Image Recognition

Intelligent cloud workflow management and scheduling method for big data applications

Intelligent Cloud Workflow Management and Scheduling Method for Big Data Applications

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