Is It All the Same? Mapping and Characterizing Deprived Urban Areas Using WorldView-3 Superspectral Imagery. A Case Study in Nairobi, Kenya

Abstract

In the past two decades, Earth observation (EO) data have been utilized for studying the spatial patterns of urban deprivation. Given the scope of many existing studies, it is still unclear how very-high-resolution EO data can help to improve our understanding of the multidimensionality of deprivation within settlements on a city-wide scale. In this work, we assumed that multiple facets of deprivation are reflected by varying morphological structures within deprived urban areas and can be captured by EO information. We set out by staying on the scale of an entire city, while zooming into each of the deprived areas to investigate deprivation through land cover (LC) variations. To test the generalizability of our workflow, we assembled multiple WorldView-3 datasets (multispectral and shortwave infrared) with varying numbers of bands and image features, allowing us to explore computational efficiency, complexity, and scalability while keeping the model architecture consistent. Our workflow was implemented in the city of Nairobi, Kenya, where more than sixty percent of the city population lives in deprived areas. Our results indicate that detailed LC information that characterizes deprivation can be mapped with an accuracy of over seventy percent by only using RGB-based image features. Including the near-infrared (NIR) band appears to bring significant improvements in the accuracy of all classes. Equally important, we were able to categorize deprived areas into varying profiles manifested through LC variability using a gridded mapping approach. The types of deprivation profiles varied significantly both within and between deprived areas. The results could be informative for practical interventions such as land-use planning policies for urban upgrading programs.

Identifying Precarious Settlements and Urban Fabric Typologies Based on GEOBIA and Data Mining in Brazilian Amazon Cities

“…The classification of DUAs typologies was performed by unsupervised k-means clustering. Similarly, both studies [48,81] identified settlements with different levels of consolidation, organization of the urban fabric, and built-up density. Note that these studies also used grids as spatial reference units.…”

“…In the Brazilian case, prior characterization of settlements is a mandatory step for local governments to access funding from the federal government's slum upgrading programs [4]. Furthermore, this type of characterization allows the understanding of internal structures in precarious areas to provide meaningful indicators to combat poverty through evidencebased policy-making toward more sustainable cities [81].…”

“…Feitosa et al [48] (by IMMerSE methodology) and Georganos et al [81] also identified morphological patterns in slums and other types of precarious settlements. Implementing the methodology in Nairobi, Kenya, Georganos et al [81] assembled multiple WorldView-3 datasets with varying numbers of bands and image features, allowing for the exploration of computational efficiency, complexity, and scalability. The authors used a land cover classification generated by the GEOBIA approach to identify typologies of deprived urban areas (DUAs).…”

Data and Urban Poverty: Detecting and Characterising Slums and Deprived Urban Areas in Low- and Middle-Income Countries

“…The SLUMAP project that focused on sub-Saharan African cities investigated the potential of EO for mapping and characterising DUAs, aiming for cost efficiency. Experiments involving machine learning algorithms and satellite imagery with different spatial resolutions showed that city-level deprivation mapping could be achieved with open, cost-free Sentinel-1/2 images, while very-high-resolution images were requested to produce detailed settlement-level characterisation (Abascal et al 2022;Georganos et al 2021;Vanhuysse et al 2021). At the city scale, the (Abascal et al 2022b) location and extent of slums within a city were modelled, while detailed characteristics of the physical environment within slums were extracted at the settlement scale.…”

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

“…It provides a 0.3 m resolution panchromatic sensor, an eight-band visible and near-infrared imager (operating between 397 and 1039 nanometres) with a ground sample distance (GSD) of 1.2 m, a short-wave infrared (SWIR) sensor with further eight bands (1184-2373 nm) at a resolution of 3.7 m, and finally the CAVIS (Clouds, Aerosol, Vapors, Ice, and Snow) sensor with twelve additional bands at 30 m for the retrieval of atmospheric properties [1]. These capabilities make it particularly suitable for application in urban areas, where the extremely complex morphology requires a higher level of detail [2,3].…”