Disaster resilience

Disaster risk is calculated using models that use different types of geodata. However, knowledge alone of an existing risk is not sufficient to reduce it and prevent or mitigate disasters. Instead, risk assessment often has to be fine-tuned or adjusted to specific locations, for example, a town or city. Then, specialists are needed to develop and execute strategies to reduce the risk. This can be done by reducing the hazard, such as giving rivers more spillover space before the water reaches the populated area. Or we can work on reducing the so-called vulnerability of people or structures, such as through better awareness-raising, or implementation of suitable building codes in areas susceptible to earthquakes.

Finally, early warning and adequate action before an impending event, such as a tropical storm, allow the evacuation of people and the securing of property. An increasing number of cities are employing dedicated resilience officers, such a those in the 100 Resilient Cities initiative (funded by the Rockefeller Foundation). Since much of risk management relates to appropriate urban planning, this learning pathway includes courses taught in Urban and Land Futures. 

The resilience managers require appropriate risk information. This is generated by experts in modelling who understand the different forms of quantitative risk modelling, or by advanced data analysis experts trained in machine learning or Big Data analysis.

In this pathway, we focus on modelling approaches using statistical information from past events, such as earthquakes or landslides, or on methods that make use of physical information, such as the specific geotechnical and topographic properties of areas susceptible to landslides.

The Resilience Modelling expert thus becomes proficient in the use, adaptation and further development of different types of models, including the relevant statistical data and machine learning methods that apply. The data analysis direction will emphasise 3D modelling with photogrammetry and computer science approaches, machine learning, and semantic scene analysis, as well as relevant statistical and programming elements. This is suitable for students who want to work in the disaster resilience domain, but who also have a strong quantitative interest, and who are interested in a career in academia, a space agency or a research institute. Students who wish to specialise in advanced data analysis will follow several courses in the GeoAI specialisation.

After completing this Master’s specialisation, you will:

• have an in-depth knowledge of state-of-the-art in the disaster risk and resilience concept, and how to quantify it, including how to model underlying hazards;
• have a comprehensive understanding of integrating and leveraging multiscale, multitemporal, and multisource data from geospatial technologies;
• have solid technical skills to be able to develop technologies, tools and algorthms useful for achieving sustainable development goals.

After successfully finishing this Master’s specialisation, you:

• can capture and generate geodata following best practices, for example to model hazards and risks, or use machinne learning to disaster damage in satellite images;
• can analyse and interpret captured multi-variate datasets to gain applicable geospatial insights, such as on the changing probability of storm occurrence under climate change conditions;
• can contribute to specialist teams in developing innovative approaches.

After completing this Master’s specialisation, you:

• are capable of objective, independent, and critical thinking;
• have developed an interest in responding to changing demands and opportunities from society and industry, both in scientific and technical terms;
• recognise the role of geo-information and earth observation in providing solutions to global challenges and sustainable development goals.