Context and objectives
It is estimated that more than one billion people worldwide reside in slums or informal settlements, with the vast majority in low- and middle-income countries (LMICs). These communities are dynamic and particularly vulnerable to environmental and man-made hazards, yet there is a major lack of reliable geospatial data to monitor how such neighbourhoods and the number of their residents evolve over time, and how they are exposed to multiple hazards. Our project aims to address this gap by developing new EO-based methods to map the dynamics of settlements with informal morphologies and their population, using freely available or low-cost satellite imagery and Machine Learning/Deep Learning techniques. Furthermore, the project will generate consistent high-resolution spatial data though the design of a framework deriving indicators of hazard susceptibility at the neighbourhood level. This will support more inclusive urban planning, and progress towards global development goals.
Project outcome
Expected scientific results
• Innovative EO approaches for deriving variables related to the space-time dynamics of urban settlements with informal morphologies, their population, and exposure to hazards;
• Insight into the potential of several super-resolution models for a real-life application in a complex urban context;
• Insight into the potential of models for capturing the space-time changes of complex urban forms;
• Advancing the population modelling field beyond the state-of-the-art by tackling some of the most challenging modelling scenarios, harnessing recent advances in DL;
• Framework for producing consistent indicators that can help monitor progress towards SDG 11.
Expected products and services
• An implementation of EO imagery super-resolution for the study of morphological informality;
• EO-based model(s) and maps to monitor the evolution of morphological informality;
• EO-based model(s) and maps to monitor the evolution of deprived urban communities;
• A reproducible workflow for deriving geospatial indicators of exposure to multiple hazards.
| Project leader(s): | ULB - IGEAT - ANAGEO (Analyse Géospatiale) | |||
| Belgian partner(s) |
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| Website: | https://dyneo4slums.ulb.be/ | |||