Earthquakes, landslides, tsunamis, and floods are just a few of the natural hazards that have been plaguing our world in recent years. Innumerable deaths, damage to property and local infrastructure, and destruction of habitats and ecosystems are among the consequences dramatically effecting life on the planet. In addition, rampant human activities, including poorly planned urban development, uncontrolled population growth, and large-scale deforestation can only lead to an increase in the frequency and potency of extreme natural hazards with unknown future consequences.

Technology, however, has provided hope, for researchers as well as relevant agencies and practitioners, for the development of solutions for mitigating and even potentially preventing natural hazards. In particular, advances in sensory technology have shown promise in applications spanning risk assessment and disaster prediction as well as for high spatio-temporal earth observation systems. Sensor technology provides the ability to measure factors and/or variables, such as latitude or slope, in hazardous areas, enabling complex and dynamic earth features to be analyzed. Nowadays, reliable sources with active and passive sensors, for example, optical, multispectral, hyperspectral, radar, or lidar sensors, and satellite, aerial, and terrestrial platforms have proven their capabilities for timely data acquisition and earth observation. Emerging intelligent machine and deep learning algorithms and advances in computer vision have also provided a technological edge for models and the development of systems for mitigating and reducing the undesirable effects of natural hazards. This also allows the development of decision support tools for the deployment of response strategies, loss estimation, and for the coordination of rescue operations.

This Special Issue focuses on recent advances in sensors and the resulting sensor technologies for natural hazard research, including practical applications of integrated and multi-sensor data fusion. This includes critical aspects of sensory data integration, which leverages advanced data processing and fusion techniques, and modelling and results visualization based on sensor-derived datasets. We welcome both original research and review articles which address existing and emerging research topics.

Potential topics include but are not limited to the following:

• Trends in sensors and data acquisition techniques such as optical, multispectral, hyperspectral, radar, and lidar systems from various platforms, such as spaceborne or airborne
• Sensor fusion algorithms
• Sensor analysis, processing, and ensemble modeling for robust information extraction
• Applications of multi-source data and information fusion for dynamic environmental modelling and natural hazard prediction
• Improving multi-source data and information fusion by exploiting machine learning, deep learning, and artificial intelligence in image processing, data integration, and classification
• Multi-sensor data integration, methodologies, evaluation, and interoperability for sustainable site monitoring and natural disaster management
• Susceptibility, hazard and risk assessment and mapping, spatial statistics, and visualization of integrated sensors, data, and information