Real-Time Natural Disasters Detection and Monitoring from Smart Earth Observation Satellite

Visser, S.J.; Dawood, Anwar

doi:10.1061/(asce)0893-1321(2004)17:1(10)

Cited by 20 publications

(9 citation statements)

References 9 publications

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“…So, the process of data downlink causes a long delay time, and it severely affects rapid response to emergencies [ 7 , 8 , 9 ]. On-board processing is a way to effectively improve response speed and provide immediate products for rapid decision-making [ 10 , 11 , 12 ]. After processing and sending the data about which we are most concerned, the amount of data can be reduced several times.…”

Section: Introductionmentioning

confidence: 99%

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Shi

Zhuang

et al. 2018

Sensors

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With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

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Section: Introductionmentioning

confidence: 99%

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Shi

Zhuang

et al. 2018

Sensors

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“…response remote sensing application is wildfire detection (Ambrosia et al 1998, Galindo et al 2003, Visser and Dawood 2004, Laneve et al 2006, Davies et al 2009). …”

Section: Remote Sensing Of Hazardsmentioning

confidence: 99%

Time-Sensitive Remote Sensing

Lippitt¹

2015

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iii Time--sensitive Remote Sensing Copyright © 2012 By Christopher D. Lippitt iv ACKNOWLEDGEMENTS This dissertation would not have happened, would have been substantially less insightful, or might have addressed a much different and no doubt less interesting topic, were it not for the following people. First and foremost, my wife Caitlin -she has been my guide and friend since I first arrived in California and has contributed immeasurably to this work in every possible capacity. Her encouragement to pursue ideas, thoughtful comments and revisions, and patience with my general disposition at times, made completing this work possible. My parents enabled this work by showing me how to live and contribute to the world without trying to influence how I did so. The completion of this dissertation is as much a product of their efforts as it is of mine.The largest single outside contributor to this work was Dr. Douglas Stow, who advised me, kept me on track, and at the same time allowed me to find my own way. Many of the ideas contained within this dissertation are his or were shaped by him and I will forever consider him a close friend to whom I owe a great deal. Dr. Keith Clarke encouraged me to explore Claude Shannon's early work and how it might inform the creation, transmission, and consumption of information from remote sensing sources, which clearly had a direct and lasting impact on this dissertation. Both Dr. Michael Goodchild and Dr. Keith Clarke espoused the intrinsic value of knowledge and encouraged big thinking and a search for theories of geographic information, which continues to impact my scientific outlook and career. Dr. Dar Roberts gave me a home and stimulating place to work and learn in Santa Barbara: the VIPER lab. He also showed me that expecting more from people can help them do things they might not have otherwise considered possible. Finally, a significant thanks is owed to Dr. Ming--Hsiang Tsou, who encouraged and enriched my thinking about information systems and distribution and the impact they can have on society.The list of people who contributed to my completion of this degree is long and impossible to complete. In particular I would like to thank the support staff at San Diego State University Department of Geography and University of California Santa Barbara Department of Geography: L. Pete Coulter, Dave McKinsey, Marcus Chiu, Patti O'Leary, Bonnie Golden, Lilia Ortiz, and last but certainly not least, Dylan Parenti. Several colleagues became close friends and contributed in a range of capacities, whether helping navigate bureaucracy, providing thoughtful feedback, or just helping me forget how much work I had to do: Grant Fraley, Marta Jankowska, Sean Crotty, Nicole Simons, Nate Royal, Pam Dalal, Cristiano Giovando, and Charlie Schmidt are but a few of these people. Geography, 2006Geography, , 2007Geography, , 2008Geography, , 2009Geography, , and 2010 First Place--AAG Human Environment Regional Observatory Fellowship; Awarded by George Perkins Marsh Institute, 2003, 2004 John ...

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“…, analysis of crowdsourced data[54][57], enhanced visualization and monitoring of different aspects[58][59], processing of sensor data for regular alerts and storage of crucial real-time and live data[60][61] can be done over the Cloud environment to stay better prepared against the disasters. Especially for the preparedness against the disasters, local computing resources, supercomputers or similar HPCs can also be used in a hybrid fashion[62][63][64].3.2.3. ResponseFor the better response, the co-ordination of the entire rescue and search operation can be centered around the Cloud infrastructure with remote operations, information collection, intuitive visualization, meaningful monitoring and efficient evacuation plans[65][54][38][66]…”

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confidence: 99%

Cloud Computing in natural hazard modeling systems: Current research trends and future directions

Garg

Hilton

et al. 2019

International Journal of Disaster Risk Reduction

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Every year, natural disasters cause major loss of human life, damage to infrastructure and significant economic impact on the areas involved. Geospatial Scientists aim to help in mitigating or managing such hazards by computational modeling of these complex events, while Information Communication Technology (ICT) supports the execution of various models addressing different aspects of disaster management. The execution of natural hazard models using traditional ICT foundations is not possible in a timely manner due to the complex nature of the models, the need for large-scale computational resources as well as intensive data and concurrent-access requirements. Cloud Computing can address these challenges with near-unlimited capacity for computation, storage and networking, and the ability to offer natural hazard modeling systems as end services has now become more realistic than ever. However, researchers face several challenges in adopting and utilizing Cloud Computing technologies in this area. Moreover, accessing the Cloud services during the disaster where the communication and power supply can break down, is still an open challenge. As such, this survey paper discusses these challenges, needs and existing problems to reflect the current research trends and outlines a conceptual Cloud-based solution framework for more effective natural hazards modeling and management systems using Cloud infrastructure in conjunction with other technologies such as Internet of Things(IoT) networks, fog and edge computing. We draw a clear picture of the current research state in the area and suggest further research directions for future systems.

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Real-Time Natural Disasters Detection and Monitoring from Smart Earth Observation Satellite

Cited by 20 publications

References 9 publications

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Time-Sensitive Remote Sensing

Cloud Computing in natural hazard modeling systems: Current research trends and future directions

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