Disaster Management

In May 2024, southern Germany experienced relentless heavy rainfall for several days, particularly affecting the regions of Baden-Württemberg and Bavaria. For effective mission planning, it is crucial for teams to have access to up-to-date data, including maps, satellite images and orthophotos. This information is provided by the Center for Satellite-Based Crisis Information (ZKI) of the German Aerospace Centre (DLR).

This article discusses the benefits of using synthetic images generated through AI in disaster management. it presents examples of usages and discusses the benefits of the approach for privacy, ethics, efficiency and more.

This article explores how command systems represent a cornerstone in large scale emergency management based on the example of the German Regulation DV 100.

Wildfires, with their devastating impact on ecosystems and communities, present a formidable challenge in natural disaster management. In response, a pioneering approach has been developed, synergizing satellite imagery, rover exploration, and sophisticated algorithms

Tuscany, Italy, known for its landscapes and rich historical heritage, recently bore the brunt of devastating floods, leaving communities in disarray in their wake. These calamitous events have once again underscored the critical need to address the escalating frequency and severity of natural disasters through innovative solutions. A key path forward involves harnessing the formidable power of technology through collaborative efforts between the public and private sectors, alongside research institutions.

Information is a critical part in the organisation and execution of work in civil protection work. First responders such as firefighters collect information on emergencies to organize their work prior and during natural disasters. From traffic accidents, forest fires, floods, to rescue operations and inspections of buildings. To prepare for forest fires, climate data, terrain data, type of incident, and cause of fire are collected. The statistics are used to identify various risks that cause incidents, try to mitigate accidents, and to train and prepare appropriate responses when fires occur. With advancements in technology, remote sensing and data collection technologies can monitor, collect and analyze data efficiently to support the decision-making process prior, and during emergencies. The TEMA project will utilize existing data sources, supplemented with additional sources to create models on the further progress of forest fires and floods.

In the past decade, we have witnessed spectacular breakthroughs achieved by deep learning in various applied and scientific domains. Despite their empirical success, neural networks are notorious for their black-box nature. Due to their inherent complexity, their inner workings are not readily comprehensible to human users. The emerging field of explainable artificial intelligence (XAI) aims to make the decision-making of such opaque models more transparent, improving trust and usefulness for human users. Meeting explainability standards has just become a legal requirement, in particular for high-stake applications, under the EU AI Act. In the TEMA project, we aim to employ such methods in the context of natural disaster management.

Natural disasters are taking place around the globe and know no geopolitical borders, which underscores the need for transnational cooperation and collaborative solutions. Against this backdrop, science diplomacy for disaster management, henceforth referred to as “disaster diplomacy”, presents an opportunity for knowledge exchange by incorporating collaboration across regional, institutional, cultural, and disciplinary sectors.