SENSOR FUSION, GIS AND AI TECHNOLOGIES FOR DISASTER MANAGEMENT

Abstract

Modern Disaster Management Systems are based on several columns that combine theory and practice, software, and hardware being under technological advance. In all parts, spatial data is key in order to analyze existing structure, assist in risk assessment and update the information after a disaster incident. This paper focus on technological advances in several fields of spatial analysis putting together the advantages, limitations and technological aspects from well-known or even innovative methods, highlighting the huge potential of nowadays technologies for the field of Disaster Risk Management (DRM).A focus then is lying on GIS and Remote Sensing technologies that are showing the potential of high-quality sensors and image products that are getting easier to access and captured with recent technology. Secondly, several relevant sensors being thermal or laser-based are introduced pointing out the application possibilities, their limits, and potential fusion of them. Emphasis is further driven to Machine Learning techniques adopted from Artificial Intelligence that improve algorithms for auto-detection and represent an important step forwards to an integrated system of spatial data use in the Disaster Management Cycle. The combination of Multi-Sensor Systems, new Platform technologies, and Machine Learning indeed creates a very important benefit for the future.