Home / Journals American Journal of Neural Networks and Applications / The Application of Artificial Intelligence in Disaster Relief Under Complex Disaster Conditions
The Application of Artificial Intelligence in Disaster Relief Under Complex Disaster Conditions
Submission DeadlineApr. 30, 2020

Submission Guidelines: http://www.sciencepublishinggroup.com/home/submission

Lead Guest Editor
Bobo Ju
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
Guest Editors
  • Chunxue Wu
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
  • Naixue Xiong
    Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, USA
  • Yan Wu
    School of Public and Environmental Affairs, Indiana University, Bloomington, USA
  • Shaochun Wu
    School of Computer Science and Technology, Shanghai University, Shanghai, China
  • Xuefeng Liang
    School of Information, Kyoto University, Kyoto, Japan
  • Hongyan Li
    State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Introduction
When disaster strikes, speed of rescue is crucial. The difference between life and death depends on the time it takes to correctly assess the damage that follows a disaster. In the event of disaster or other extraordinary circumstances (including natural disasters, accidents, emergencies, etc.), obtaining real-time rescue operations is essential to save lives and public property safety. And the scientific rescue business can achieve long-term perfect preparation and timely implementation of rescue operations anytime and anywhere. The real time and rapid rescue industry is a scientific problem, which is based on the perfect system, system or chain, systematic and large-scale model of itself, and can be prepared for a long time, carry out rescue operations anytime and anywhere, and finally realize the benefits of all parties. Emergency workers need to overcome the disruption of local communications and transportation infrastructure, which makes accurate assessment dangerous, difficult and slow. Although satellite and aerial images offer more ground-based and less risky alternatives, analysts still need to conduct time-consuming manual assessments of the images. Computer vision algorithms for locating and classifying various building damage in natural disasters to speed up analysis of satellite and aerial images are essential for disaster relief. This topic focuses on the new rescue technology discussion of artificial intelligence technology, big data, sensors, unmanned aerial vehicles and robots in post-disaster rescue.
Aims and Scope:
  1. Application of the combination of artificial intelligence and remote sensing technology in rapid disaster assessment
  2. Application of new special robot technology in disaster relief
  3. Big data situational awareness search and rescue personnel distribution
  4. The combination of big data and machine learning to perceive potential secondary disasters after disasters
  5. Application of machine learning and new UAV cluster technology in distribution of disaster relief materials
  6. Application of intelligent Internet of things technology in digital disaster relief
Guidelines for Submission
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(see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=339).

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