Disruptive Technology for Disaster Risk Management in Africa
Background and context
Population growth and demographic shifts on the African continent are two of the most significant structural changes taking place in the 21st century. Based on the latest demographic projections, Africa will soon account for 41% of the working-age population worldwide. The continent will grow to an estimated total of 4.2 billion people by 2100; and most of this population will settle in cities. A key challenge in risk reduction is the timely collection of actionable risk data. These data include information on socio-economic activity such as the concentration of people, housing and facilities. Available population estimates are unstandardized, and most efforts focus on providing data (such as estimated population) on the larger megacities. The growing population in secondary and tertiary cities is often overlooked even though almost half of the world’s urban dwellers live in settlements of less than 500,000 people . Small and medium-sized cities are also growing faster than large and mega-cities; yet are more vulnerable due to limited data, political power, personnel and resources . New approaches are needed that can significantly scale the coverage and frequency of data collection whilst also lowering the costs and complexities involved in order to integrate risk considerations into the urbanization process.
This proposed project under the ACP-EU NDRR Program aims to demonstrate new disruptive technology services that address the data gaps in managing exposure and climate risks in Sub-Saharan Africa (SSA)’s fastest growing cities. The demonstration benchmark includes the delivery of prototype products for at least 15 cities . The project would develop operational services that harness new technological opportunities in risk mapping at continental, metropolitan and local scales. These new technologies are based on the recent and significant improvements in satellite image acquisition, drone mapping and survey applications and artificial intelligence for image classification and analysis.
This project is in its inception phase.
Window of Action
- Window 1
- 07/2019 - 02/2020