Automated cyclone discovery and tracking using knowledge sharing in multiple heterogeneous satellite data

Shen Shyang Ho, Ashit Talukder

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

12 Scopus citations

Abstract

Current techniques for cyclone detection and tracking employ NCEP (National Centers for Environmental Prediction) models from in-situ measurements. This solution does not provide true global coverage, unlike remote satellite observations. However it is impractical to use a single Earth orbiting satellite to detect and track events such as cyclones in a continuous manner due to limited spatial and temporal coverage. One solution to alleviate such persistent problems is to utilize heterogeneous sensor data from multiple orbiting satellites. However, this solution requires overcoming other new challenges such as varying spatial and temporal resolution between satellite sensor data, the need to establish correspondence between features from different satellite sensors, and the lack of definitive indicators for cyclone events in some sensor data. We describe an automated cyclone discovery and tracking approach using heterogeneous near real-time sensor data from multiple satellites. This approach addresses the unique challenges associated with knowledge discovery and mining from heterogeneous satellite data streams. We consider two remote sensor measurements in our current implementation, namely: QuikSCAT wind satellite measurements, and merged precipitation data from TRMM and other satellites. More satellites will be incorporated in the near future and our solution is sufficiently powerful that it generalizes to multiple sensor measurement modalities. Our approach consists of three main components: (i) feature extraction from each sensor measurement, (ii) an ensemble classifier for cyclone discovery, and (iii) knowledge sharing between the different remote sensor measurements based on a linear Kalman filter for predictive cyclone tracking. Experimental results on historical hurricane datasets demonstrate the superior performance of our approach compared to previous work.

Original languageEnglish (US)
Title of host publicationKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Pages928-936
Number of pages9
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
CountryUnited States
CityLas Vegas, NV
Period8/24/088/27/08

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All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Cite this

Ho, S. S., & Talukder, A. (2008). Automated cyclone discovery and tracking using knowledge sharing in multiple heterogeneous satellite data. In KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining (pp. 928-936). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1401890.1402001