A Geoinformatics Approach for the Analysis of Remote Sensing, Model and Social Media ‘Big Data’ to Study Environmental Hazards
Guido Cervone, Pennsylvania State University
Friday, October 25, 2013
11am
EBII 3211 — NCSU Centennial Campus
(Directions to Centennial campus and parking information)
This talk is part of the Taming the Data invited-speaker series, held in the Department of Computer Science at NC State University.
Talk Title: A Geoinformatics Approach for the Analysis of Remote Sensing, Model and Social Media ‘Big Data’ to Study Environmental Hazards
A video of this talk is available here.
Talk abstract:
In recent years, the advances in our ability to observe the Earth and its environment through the use of air, space and ground based sensors has led to the generation of large dynamic, and geographically distributed spatiotemporal data. New challenges arise from an unprecedented access to massive amounts of Earth science data that are quickly leading towards a data-rich but knowledge-poor environment.
The rate at which geospatial data are being generated exceeds our ability to organize and analyze them to extract patterns critical for understanding our dynamically changing world. Geoinformatics algorithms are needed to address these scientific and computational challenges and provide innovative and effective solutions to analyze these large, often multi-modal, spatiotemporal datasets.
In this talk I will preset selected results of my recent research to use remote sensing, numerical models and social media data to study environmental hazards.
About the speaker:
Dr. Guido Cervone will start as associate professor of geoinformatics in the Department of Geography and Institute for CyberScience at the Pennsylvania State University in January 2014. He is also affiliate scientist with the Research Application Laboratory (RAL) at the National Center of Atmospheric Research (NCAR). He sits on the advisory committee of the United Nation Environmental Programme (UNEP), Division of Disasters and Early Warning Assessment (DEWA). His research is currently being funded by the Department of Transportation and by the Office of Naval Research.
His research expertise is in machine learning and geoinformatics, and his main interest is the mining of spatial and temporal remote sensing, model and social media big data associated with natural, man-made, and technological hazards. He worked on the theoretical development and implementation of symbolic and evolutionary machine learning systems. He developed a new methodology based on non-Darwinian evolution to identify the source characteristics of an unknown toxic atmospheric release. He sailed over 4000 offshore miles.
This invited-speaker series has been made possible thanks to generous support from:
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