Locating visual storm signatures from satellite images

Yu Zhang, Stephen Wistar, Jose A. Piedra-Fernandez, Jia Li, Michael A. Steinberg, James Z. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Weather forecasting is a problem where an enormous amount of data must be processed. Severe storms cause a significant amount of damages and loss every year in part due to the insufficiency of the current techniques in producing reliable forecasts. We propose an algorithm that analyzes satellite images from the vast historical archives to predict severe storms. Conventional weather forecasting involves solving numerical models based on sensory data. It has been challenging for computers to make forecasts based on the visual patterns from satellite images. In our system we extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. Particularly, the algorithm extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages711-720
Number of pages10
ISBN (Electronic)9781479956654
DOIs
StatePublished - Jan 1 2014
Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
Duration: Oct 27 2014Oct 30 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
CountryUnited States
CityWashington
Period10/27/1410/30/14

Fingerprint

Satellites
Weather forecasting
Numerical models
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

Cite this

Zhang, Y., Wistar, S., Piedra-Fernandez, J. A., Li, J., Steinberg, M. A., & Wang, J. Z. (2014). Locating visual storm signatures from satellite images. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, ... R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 711-720). [7004295] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004295
Zhang, Yu ; Wistar, Stephen ; Piedra-Fernandez, Jose A. ; Li, Jia ; Steinberg, Michael A. ; Wang, James Z. / Locating visual storm signatures from satellite images. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. editor / Wo Chang ; Jun Huan ; Nick Cercone ; Saumyadipta Pyne ; Vasant Honavar ; Jimmy Lin ; Xiaohua Tony Hu ; Charu Aggarwal ; Bamshad Mobasher ; Jian Pei ; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 711-720 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).
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abstract = "Weather forecasting is a problem where an enormous amount of data must be processed. Severe storms cause a significant amount of damages and loss every year in part due to the insufficiency of the current techniques in producing reliable forecasts. We propose an algorithm that analyzes satellite images from the vast historical archives to predict severe storms. Conventional weather forecasting involves solving numerical models based on sensory data. It has been challenging for computers to make forecasts based on the visual patterns from satellite images. In our system we extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. Particularly, the algorithm extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts.",
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Zhang, Y, Wistar, S, Piedra-Fernandez, JA, Li, J, Steinberg, MA & Wang, JZ 2014, Locating visual storm signatures from satellite images. in W Chang, J Huan, N Cercone, S Pyne, V Honavar, J Lin, XT Hu, C Aggarwal, B Mobasher, J Pei & R Nambiar (eds), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004295, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 711-720, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004295

Locating visual storm signatures from satellite images. / Zhang, Yu; Wistar, Stephen; Piedra-Fernandez, Jose A.; Li, Jia; Steinberg, Michael A.; Wang, James Z.

Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Wo Chang; Jun Huan; Nick Cercone; Saumyadipta Pyne; Vasant Honavar; Jimmy Lin; Xiaohua Tony Hu; Charu Aggarwal; Bamshad Mobasher; Jian Pei; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2014. p. 711-720 7004295 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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Zhang Y, Wistar S, Piedra-Fernandez JA, Li J, Steinberg MA, Wang JZ. Locating visual storm signatures from satellite images. In Chang W, Huan J, Cercone N, Pyne S, Honavar V, Lin J, Hu XT, Aggarwal C, Mobasher B, Pei J, Nambiar R, editors, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 711-720. 7004295. (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). https://doi.org/10.1109/BigData.2014.7004295