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.
Highlights
Despite complexand heterogeneity of the landscape in deprived urban areas (DUAs), the unsupervised segmentation apity and heterogeneity of urban the urban landscape in DUAs, the unsupervised segmentation peared satisfactory as the produced segments represented whole, or parts of, land surface appeared satisfactory as the produced segments represented whole, or parts of, land surobjects, such such as building roofs roofs and trees
Our work has provided a novel framework with which to characterize deprived urban areas (DUAs) through Earth observation (EO) datasets
We tailored a GEOBIA processing chain to our requirements for mapping the specificities of the land cover in DUAs. We considered factors such as model complexity and the computational burden; we endeavored to favor the potential of the transferability of the whole process
Summary
Over the past few decades, Sub-Saharan Africa (SSA) has been facing an extensive and overwhelming population growth, mainly occurring in urban regions [1]. The lack of provisions to address this phenomenon has further exaggerated socio-economic fragmentation within cities [2], leading to the proliferation of deprived urban areas (DUAs) that often lack basic services, such as access to clean water and sanitation, among others [3]. Within DUAs, urban dwellers are often exposed to unhealthy and unsuitable physical environments, with hazardous effects on their health. As pointed out by Aliu et al [4] in a case study on Lagos, Nigeria, residents of the most deprived areas of the Remote Sens.
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