Sensor Based Computer Vision for Safety and Health Monitoring in Disaster Risk Management
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 20 May 2026 | Viewed by 17
Special Issue Editor
Interests: image and video processing; semantic analysis; neural networks; 3-D data processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to announce a Special Issue of Sensors on “Sensor Based Computer Vision for Safety and Health Monitoring in Disaster Risk Management”.
The increasing frequency and intensity of natural and human-made disasters underline the urgent need for advanced monitoring and response systems. Sensors and computer vision technologies are playing a vital role in enhancing situational awareness, supporting decision making, and safeguarding both human lives and critical infrastructure. From UAVs and satellite-based imaging to wearable sensors and IoT-enabled networks, the integration of sensor data with computer vision methods opens new possibilities for real-time risk detection, early warning, and adaptive disaster response.
This Special Issue aims to bring together contributions that highlight recent advances in sensor-based computer vision applications for disaster risk management, with particular emphasis on safety and health monitoring. Topics of interest include, but are not limited to, the following:
- Vision-based sensing for early detection of hazards and risks;
- Ambient sensors for health monitoring of first responders and affected populations;
- UAV, satellite, and ground-based vision systems for disaster mapping and situational awareness;
- Data fusion from multimodal sensors for improved decision support;
- AI and machine learning approaches for risk prediction and real-time analysis;
- Applications in search and rescue, structural safety assessment, and environmental monitoring.
We invite original research articles, reviews, and case studies that contribute to advancing knowledge and practice in this interdisciplinary field. Contributions that demonstrate innovative methodologies, real-world deployments, and cross-sectoral collaborations are especially encouraged.
We look forward to your valuable submissions and to building a collection that will serve as a reference point for researchers, practitioners, and policy-makers working toward safer and more resilient societies.
Dr. Nicholas Vretos
Guest Editor
Manuscript Submission Information
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Keywords
- sensor-based computer vision
- disaster risk management
- safety and health monitoring
- multimodal sensing
- situational awareness
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