Deep neural networks have shown great empirical success in the last years, but their inner working often remained elusive. XAI methods aim to open up the black box of such opaque models at the heart of many modern machine learning and artificial intelligence applications.
When a natural disaster occurs, it is particularly important to obtain information about which regions are affected as quickly as possible. Remote sensing data, especially from earth observation satellites, is often used to analyse the course of natural disasters. However, most satellites are on fixed orbits or have to be explicitly tasked in order to take images of a certain region. This always results in a time delay.
Significant progress has already been made in the area of data analysis in recent years, which has led to a speeding up of the process. However, the activation of satellite-based emergency mapping (SEM) is still a manual action by an authorised user. The identification of an area of interest (AOI) for explicit tasking, i.e. controlling the satellite for image acquisition, is also necessary in advance.
In June 2021, Germany experienced the biggest natural disaster in its recent history due to flash floods that hit Western Germany. Experts say that floods like this occur once in 1000 years. Two days before the flood, a warning was issued by the German Weather Service (DWD) predicting 80 to 180 litres of rain per square meter, but that amount was exceeded the following night with 200 litres per square meter. Hot and dry weather in the weeks prior to the floods dried the soil and reduced its capacities to absorb the rainwater, which further intensified the floods in many areas. Places in North Rhine-Westphalia and Rhineland-Palatinate were particularly affected.
Since the emergence of the first camera and radio systems over a century ago, media systems have played an essential part in our society. Not only for entertainment purposes, but also for documentation, communication, analysis, and training. Especially during disasters and emergencies, media has become a primary tool transporting information, documenting situations, planning responses, as well as predicting problems and allowing preemptive decision-making.
The utilization of novel advancements such as Artificial Intelligence (AI), extensive datasets (big data), and three-dimensional visualizations is being observed.
In recent years, the rise of various innovative forecasting approaches, including the utilization of big data, has significantly bolstered disaster management for a wide range of natural hazards. These approaches leverage cutting-edge technologies and interdisciplinary collaborations to enhance the accuracy and timeliness of hazard predictions, thereby minimizing the potential impact on vulnerable communities.
The climate is quickly changing. Almost every day we are informed by television and newspapers about a natural disaster. Just to mention what happened this summer, Greece [1] is enduring the hottest July of the last 50 years due to a heatwave longer than six days. Northern Italy [2] is living in a different situation. Hail as big as tennis balls, winds as fast as a supercar. The Milan sky became dark at midday and the hinterland was affected by a tornado. The Mediterranean [11] Sea reached its highest ever recorded temperature at over 28.71c, beating the previous record of 28.25C set in 2003. The summer will still have much to say.
