Conferences related to Disaster Detection

Back to Top

ICC 2021 - IEEE International Conference on Communications

IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.


2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2020 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions.


2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

The aim of the conference will be to bring together the majority of leading expert scientists, thought leaders and forward looking professionals from all domains of Intelligent Transportation Systems, to share ongoing research achievements, to exchange views and knowledge and to contribute to the advances in the field. The main theme of the conference will be “ITS within connected, automated and electric multimodal mobility systems and services”.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


More Conferences

Periodicals related to Disaster Detection

Back to Top

Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Control Systems Technology, IEEE Transactions on

Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.


Geoscience and Remote Sensing Letters, IEEE

It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


More Periodicals

Most published Xplore authors for Disaster Detection

Back to Top

Xplore Articles related to Disaster Detection

Back to Top

Analysis of satellite images for disaster detection

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016

Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers, followed by max-pooling layers ...


Disaster detection system using Arduino

2017 International Conference on Information Communication and Embedded Systems (ICICES), 2017

The author aims to reduce the number of disasters drastically in order to come up with a safer and secure environment. This paper describes a system which detects the possible disasters that one can face in a household or work-space. It is an Arduino based Disaster Detection System that contains sensors for detecting the disasters. This system is new in ...


Development of Emergency Rescue Evacuation Support System (ERESS) in Panic-Type Disasters: Disaster Detection by Positioning Area of Terminals

2013 42nd International Conference on Parallel Processing, 2013

Previously, the authors have proposed the Emergency Evacuation Support System (ERESS) for reducing disaster damage. The ERESS runs under Mobile Ad-hoc Networks (MANET) and primarily aims to reduce the number of victims in panic- type disasters. This system uses ERESS Mobile Terminals (EMT) which are mobile terminals assuming smartphones and tablets. EMT is provided with an advanced disaster detection algorithm ...


Natural disaster detection using wavelet and artificial neural network

2015 Science and Information Conference (SAI), 2015

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: Indo-Australian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the ...


Behavior recognition and disaster detection by the abnormal analysis using SVM for ERESS

2018 International Conference on Information Networking (ICOIN), 2018

Many lives have been lost for many years in all parts of the world by the sudden disasters such as fire and terrorism. Main causes of the damage expansion in these disasters include the escape delay of the evacuees. To support evacuation safely and quickly is one of the effective measures to reduce the victim by the disaster. So, we ...


More Xplore Articles

Educational Resources on Disaster Detection

Back to Top

IEEE.tv Videos

Learning Lessons from Katrina
Group on Earth Observations (GEOSS)
An FPGA-Quantum Annealer Hybrid System for Wide-Band RF Detection - IEEE Rebooting Computing 2017
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
The MOVE Truck Disaster Relief Vehicle: 2017 Brain Fuel President's Chat
Group on Earth Observations(GEOSS): Applications
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
Critical use cases for video capturing systems in autonomous driving applications
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Developing Automated Analysis Tools for Space/Time Sidechannel Detection - IEEE SecDev 2016
IEEE Medal for Environmental and Safety Technologies - Jerome Faist and Frank K. Tittell - 2018 IEEE Honors Ceremony
Disaster Meets Engineering: TechNews on IEEE.tv
Bari-Bari-II: Jack-Up Rescue Robot with Debris Opening Function
Large UAS Support: Non Terrestrial Networks - Dallas Brooks - B5GS 2019
An IEEE IPC Special Session with X. Chen from Nokia Bell Labs
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
ASC-2014 SQUIDs 50th Anniversary: 4 of 6 - Keiji Enpuku
Levente Klein: Drone-based Reconstruction for 3D Geospatial Data Processing: WF-IoT 2016

IEEE-USA E-Books

  • Analysis of satellite images for disaster detection

    Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers, followed by max-pooling layers after each convolutional layer, and 2 fully connected layers. We created our own disaster detection training data patches, which is currently focusing on 2 main disasters in Japan and Thailand: landslide and flood. Each disaster's training data set consists of 30000~40000 patches and all patches are trained automatically in CNN to extract region where disaster occurred instantaneously. The results reveal accuracy of 80%~90% for both disaster detection. The results presented here may facilitate improvements in detecting natural disaster efficiently by establishing automatic disaster detection system.

  • Disaster detection system using Arduino

    The author aims to reduce the number of disasters drastically in order to come up with a safer and secure environment. This paper describes a system which detects the possible disasters that one can face in a household or work-space. It is an Arduino based Disaster Detection System that contains sensors for detecting the disasters. This system is new in the sense that it incorporates detection of more than one disaster with one device and still proves to be as cheap as possible. It is also unique in the sense that it automatically informs the emergency services when a disaster is detected.

  • Development of Emergency Rescue Evacuation Support System (ERESS) in Panic-Type Disasters: Disaster Detection by Positioning Area of Terminals

    Previously, the authors have proposed the Emergency Evacuation Support System (ERESS) for reducing disaster damage. The ERESS runs under Mobile Ad-hoc Networks (MANET) and primarily aims to reduce the number of victims in panic- type disasters. This system uses ERESS Mobile Terminals (EMT) which are mobile terminals assuming smartphones and tablets. EMT is provided with an advanced disaster detection algorithm and acquires sensor information such as acceleration, direction difference and walking steps. However, disaster detection of conventional ERESS is needed the information of all members existing on the same floor. In this paper, we propose a new disaster detection method using the positioning area information. Location information of the terminal holders are obtained by Radio Frequency Identification (RFID). In this method, we separate a number of areas in the floor and detect a disaster by the information of evacuees in the presence of each area. We show the effectiveness of the proposed method by panic-type experiments.

  • Natural disaster detection using wavelet and artificial neural network

    Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: Indo-Australian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes.

  • Behavior recognition and disaster detection by the abnormal analysis using SVM for ERESS

    Many lives have been lost for many years in all parts of the world by the sudden disasters such as fire and terrorism. Main causes of the damage expansion in these disasters include the escape delay of the evacuees. To support evacuation safely and quickly is one of the effective measures to reduce the victim by the disaster. So, we develop the system named Emergency Rescue Evacuation Support System (ERESS) as a system to detect a disaster using handheld terminals quickly and to guide to safety zone. This system automatically detects a disaster by analysis of the information of terminal holders, and sharing information with neighboring terminals. This paper focuses on behavior analysis of terminal holders and disaster detection which is big characteristics of ERESS. We propose an activity recognition using Support Vector Machine (SVM) and a disaster detection method by the abnormal analysis using SVM. The results of the performance evaluation by two experiments show the validity of the proposed method.

  • Disaster detection from aerial imagery with convolutional neural network

    In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%-90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.

  • Smart Disaster Detection and Response System for Smart Cities

    Every year, natural and human-induced disasters result in infrastructural damages, monetary costs, distresses, injuries and deaths. Unfortunately, climate change is strengthening the destructive power of natural disasters. In this context, Internet-of-Things (IoT)-based disaster detection and response systems have been proposed to cope with disasters and emergencies by improving the disaster detection and search and rescue missions during disaster response. Accordingly, IoT devices are used to collect data and help to identify hazards after disasters and to localize injured people. However, a solely IoT-based detection and response system will not be totally suitable for emergency response in smart cities, as the lack of connectivity with IoT devices might occur, due to breakages in communication infrastructures or network congestions. Therefore, we propose a novel architecture for smart disaster detection and response system for smart cities. We discuss the main building blocks of our envisioned smart system, as well as the critical challenges that will be faced ahead to implement our smart system.

  • Disaster detection in magnetic induction based wireless sensor networks with limited feedback

    The use of magnetic induction (MI) based transmissions in challenging environments has been investigated in various works. Recently, a system model has been proposed, which explains how the MI based transmission channel depends on the chosen system parameters. In order to make the system robust against environmental changes, the system parameters like resonance frequency and modulation scheme need to be properly adapted to the current channel state. It is frequently assumed, that perfect channel state information (CSI) is available at the transmitter and at the receiver. However, in practical systems this knowledge may not always be easily acquired. In addition, a permanent feedback signaling is needed in order to update the CSI at the transmitter, which usually causes interference to the surrounding devices and reduces the energy efficiency. In this paper, we investigate the potential of a recently proposed approach for channel estimation within the MI transmitter circuit without explicit feedback signaling of CSI. This technique seems promising especially for disaster detection in wireless underground sensor networks, which is the main focus of this work.

  • An mmWave beamforming scheme for disaster detection in high speed railway

    As the operating speed of high speed railway (HSR) ever increases, the train operation safety requirement is getting stricter and stricter. Among the various environment monitoring technologies, the microwave radar detection technology tends to civilian use and plays an important role in environmental safety monitoring. Though rich continuous spectrum resources are available in millimeter wave (mmWave) bands, in order to overcome unfavorable path loss, directional beamforming is usually used as an essential technology to concentrate radio signal radiation energy. To ensure the safety of train operation, we propose an mmWave beamforming scheme for railway disaster detection. In this proposed scheme, the concerned area around railways is divided into different detection areas with different danger sensitivity levels. Considering propagation characteristics of radio signals with different frequency bands within a wide frequency range, the antenna array generates multiple beams with different beam widths in different frequency bands simultaneously, and the multiple beams are responsible for different detection areas. Moreover, different detection resolutions are set for different detection areas to apply to different beam scanning schemes. Performance analysis results have demonstrated that the proposed beamforming scheme can not only greatly improve the detection efficiency, but also decrease the false alarm rate.

  • Clustering control in isopleth-oriented ad hoc communication for disaster detection

    Many people around the world are adversely affected by various unforeseen disasters such as earthquakes, fires, and acts of terror. To resolve these problems, Kansai University has proposed the Emergency Rescue Evacuation Support System (ERESS). We propose a novel isopleth-oriented multi-hop communication method using clustering control to reduce the amount of data traffic by allowing only parent terminals to exchange and share data.



Standards related to Disaster Detection

Back to Top

No standards are currently tagged "Disaster Detection"


Jobs related to Disaster Detection

Back to Top