Dr. Xu Zhong to Present Real-time Estimation of Wildfire Perimeters with Crowdsourcing at Locate16

Dr. Xu Zhong will present within the PROTECT stream at the Locate16 Conference on 12-14 April 2016. The 3-day Conference will present leading industry professionals and researchers focusing on disruptive technology and how it is building a smarter society.

Dr. Xu ZhongPresenter Bio
Dr. Xu Zhong received the Bachelor degree in Applied Physics from the University of Science and Technology of China in 2009. He completed the Ph.D. degree in Electronic Engineering from the University of Melbourne in 2014. During his Ph.D., he investigated a novel plant water-stress sensor using laser speckle analysis techniques. He developed a prototype sensor customized for leaf measurements and novel image processing algorithms which improved both the reliability and robustness of speckle analysis.
Xu Zhong is currently a postdoctoral research fellow at the Department of Infrastructure Engineering, University of Melbourne, working on the Resilient Information Systems for Emergency Response (RISER) project. His contributions led the RISER project to two major awards at the 2015 Victorian Spatial Excellence Awards (VSEA): the Award for Technical Excellence and the Victorian Government Award for Spatial Excellence, the most prestigious award of the night. His works on the RISER project produced several academic peer-reviewed papers (some under review). His current research interests lie in the area of disaster management, wireless sensor networks, geospatial data analysis, spatiotemporal data stream analysis, crowdsourcing data analysis, and image processing.

Abstract Title:  Real-time estimation of wildfire perimeters from curated crowdsourcing

Abstract Overview

Natural disasters, such as wildfires, have significant impacts upon human lives, critical infrastructure, and delicate environments worldwide. Timely and accurate information about the areas affected by ongoing natural disasters assists emergency responders and the general public to minimise these impacts. However, in the unexpected and extreme events surrounding an emergency, reliable and up-to-date information about the affected areas often becomes scarce. During the devastating 2009 Black Saturday wildfires in Victoria, Australia, for example, information about the current extents of multiple wildfires was not always available. Authoritative information sources can suffer from bottlenecks and delays, while user-generated social media data usually lacks the necessary structure and trustworthiness for reliable automated processing. We developed an automated data mining technique for realtime tracking of wildfire perimeters based on “curated” crowdsourced data about telephone calls to the emergency services. Being crowdsourced, this data is more plentiful and more rapidly available than authoritative information sources during an emergency.

On the other hand, because emergency calls are handled by trained professional operators, the data generated is more structured and reliable than social media data. The Country Fire Authority (CFA) is providing anonymized emergency calls data within its publicly available RSS feed. Using this feed, our data mining algorithm can automatically detect and track wildfire perimeters, in real time, and with relatively high accuracy. Accuracy can be further increased through combination with other authoritative demographic and environmental information, such as population density and dynamic wind fields.

Our wildfire perimeter estimator has been validated against the post-fire reconstructed perimeter of the devastating Black Saturday wildfires (2009) and Mickleham-Dalrymple wildfires (2014) in Victoria, Australia. The results show how such publicly available “curated” crowdsourced data can provide an important source of up-to-date information about an evolving emergency.

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