There exist several definitions of slums, but in the context of this project we will refer to a ‘slum’ as being strictly a ‘morphological slum’, i.e., the morphological characteristics that can be observed on remote sensing images, such as the relative higher built-up densities, the lack of ‘formal vegetation’ (compared to regular built-up areas), the small building footprints and irregular layout patterns of areas, which commonly very well coincide with slum areas as defined by UN-Habitat using the five dimensions of slums (access to water, sanitation, overcrowding, housing durability and tenure).
Existing studies usually aim at providing information on the location and extension of slums within the city, or at characterizing the physical environment of slums (i.e., in terms of greenness, built-up density, lacunarity, etc.). In this study, we propose a general framework that allows for the integration of all this information at a reduced cost. First, the location and extent of slums are mapped citywide, using free or low-cost images (HR). This enables us to limit the acquisitions of more expensive imagery (VHR) only to slums for mapping and characterizing them in detail with SoA methods and FOSS solutions.
Our methods will be tested on a set of three sub-Saharan cities, each with slums presenting different characteristics, namely Ouagadougou (Burkina Faso), Nairobi (Kenya) and Dar es Salaam (Tanzania). The project will generate various deliverables under the form of GIS layers, thematic maps, databases and tools.
The project is structured according to several work packages:
The potential of high-resolution imagery (ranging from 10m Sentinel-2 to 1.5m SPOT6-7) for scalable and transferable slum delineation will be assessed. We will carry out systematic exploration using FOSS, varying data sets and methods.
We will investigate the potential of very-high resolution imagery (ranging from 1.5m SPOT6-7 to 0.30m WorldView-3) for scalable and transferable slum characterization. State-of-the-art methods based on the use of FOSS will be used to extract a set of indicators from varying data sets, and a cost-benefit analysis will be performed.
This work package will link the technology with information needs at different levels and will ensure that research developments contribute to addressing information gaps for end users. So far, not much is known about what information is really required at the national, local and community levels, and how far remote sensing could go to reliably provide them. Ethics considerations on how to make spatial information on slums publicly available in support of pro-poor policies will be developed. The outcome of this circular feedback with end users will lead to the development of a general methodological framework.
A performance assessment will be conducted to compare the different methods and spatial/spectral resolutions. For each case study, the validation will help identify the best-performing approaches. The added value of the higher resolution against acquisition costs and computational resources will be quantified.