In a recent publication, AUTH (TEMA coordinator) proposed a fully-fledged privacy-assessment that could be applied to future uses of Autonomous Systems (AS) for Natural Disaster Management (NDM) purposes.
The articles seeks to illustrate the methodology used for the elaboration of the risk map, starting from cartographic bases, in order to obtain a product in raster format that allows rapid spatial analysis in a GIS environment for processing. It is based on the work previously presented "Methodology and Processing of the Model for the Forest Fire Risk Map".
This article explores how technology can improve natural disaster management in Finland, making a connection with European areas that, like Finland, were historically less bound to experience natural disaster but that are now facing issues as consequence of climate change.
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.