Purpose

This study will combine for the first-time cutting-edge EO techniques and Citizen Science to deepen our scientific understanding of the vulnerability of slum areas to climate change and provide local communities with insights into local risks based on which climate action can be taken.k

The project is timely considering the need for actions against climate vulnerability and will be conducted by two PhD students and supervised by three experts from both the EO, urban deprivation and CS fields. The team has a diverse background (Citizen Science, EO technologies, Hazard Modelling) to address the challenges of

• lack of spatial data creation and collection
• lack of data management and analysis
• lack of the ability to translate spatial data into relevant policy

In this project, a novel approach will be developed and tested that combines EO data and Citizen Science (CS) data for training deep learning models in order to map the degree of vulnerability of slum communities to climate-related risks across both large and secondary cities in Africa. We will focus on floods as it is a main climate-related risk in many cities. The project is a multidisciplinary research situated at the intersection between EO, CS and climate vulnerability disciplines.