Big Data Analysis in UAV Surveillance for Wildfire Prevention and Management

While wildfires continue to ravage our world, big data analysis aspires to provide solutions to complex problems such as the prevention and management of natural disasters. In this study, we illustrate a state-of-the-art approach towards an enhancement of UAV (Unmanned Aerial Vehicle) surveillance for wildfire prevention and management through big data analysis. Its novelty lies in the instant delivery of images taken from UAVs and the (near) real-time big-data oriented image analysis. Instead of relying on stand-alone computers and time-consuming post-processing of the images, a big data cluster is used and a MapReduce algorithm is applied to identify images from wildfire burning areas. Experiments identified a significant gain regarding the time needed to analyze the data, while the execution time of the image analysis is not affected by the size of the pictures gathered by the UAVs. The integration of UAVs, Big Data components and image analysis provides the means for wildfire prevention and management authorities to follow the proposed methodology to organize their wildfire management plan in a reliable and timely manner. The proposed methodology highlights the role of Geospatial Big Data and is expected to contribute towards a more state-of-the-art knowledge transfer between wildfire confrontation operation centers and firefighting units in the field.

[1]  Ferda Ofli,et al.  Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response , 2016, Big Data.

[2]  Turgay Çelik,et al.  Fire Pixel Classification using Fuzzy Logic and Statistical Color Model , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  Lori Bowen Ayre,et al.  Open Data: What It Is and Why You Should Care , 2017, Public Libr. Q..

[4]  Youmin Zhang,et al.  Vision-based forest fire detection in aerial images for firefighting using UAVs , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[5]  Lizhe Wang,et al.  Towards building a multi‐datacenter infrastructure for massive remote sensing image processing , 2013, Concurr. Comput. Pract. Exp..

[6]  Kostas Kalabokidis,et al.  Porting of a wildfire risk and fire spread application into a cloud computing environment , 2014, Int. J. Geogr. Inf. Sci..

[7]  Robert S. Allison,et al.  Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring , 2016, Sensors.

[8]  Bernhard Rinner,et al.  Networked UAVs as aerial sensor network for disaster management applications , 2010, Elektrotech. Informationstechnik.

[9]  Youmin Zhang,et al.  A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques , 2015 .

[10]  Marinos Themistocleous,et al.  Wildfire Prevention in the Era of Big Data , 2017, EMCIS.

[11]  Peter Baumann,et al.  Big Data Analytics for Earth Sciences: the EarthServer approach , 2016, Int. J. Digit. Earth.

[12]  Robin R. Murphy,et al.  CONOPS and autonomy recommendations for VTOL small unmanned aerial system based on Hurricane Katrina operations , 2009 .

[13]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[14]  Aníbal Ollero,et al.  Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement , 2022 .

[15]  Marinos Themistocleous,et al.  THE EMERGENCE OF SOCIAL MEDIA FOR NATURAL DISASTERS MANAGEMENT: A BIG DATA PERSPECTIVE , 2018 .

[16]  Kelly Gates,et al.  Big Data Surveillance: Introduction , 2014 .

[17]  Muhammad Imran,et al.  Damage Assessment from Social Media Imagery Data During Disasters , 2017, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[18]  L. Tang,et al.  Drone remote sensing for forestry research and practices , 2015, Journal of Forestry Research.

[19]  Eve Hinkley,et al.  USDA forest service–NASA: unmanned aerial systems demonstrations – pushing the leading edge in fire mapping , 2011 .

[20]  A. R. Proto,et al.  Forest and UAV: a bibliometric review , 2016 .

[21]  Youmin Zhang,et al.  UAV-based forest fire detection and tracking using image processing techniques , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[22]  M. Abidi,et al.  An Overview of Color Constancy Algorithms , 2006 .

[23]  Robin R. Murphy,et al.  Cooperative use of unmanned sea surface and micro aerial vehicles at Hurricane Wilma , 2008 .

[24]  R. Dunford,et al.  Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest , 2009 .

[25]  Gang Hua,et al.  Towards large scale land-cover recognition of satellite images , 2011, 2011 8th International Conference on Information, Communications & Signal Processing.

[26]  Z. Fu,et al.  LITERATURE REVIEW OF FIRE RISK ASSESSMENT METHODOLOGIES , 2004 .

[27]  Christine Connolly,et al.  A study of efficiency and accuracy in the transformation from RGB to CIELAB color space , 1997, IEEE Trans. Image Process..