Extracting Urban Deprivation Indicators Using Superspectral Very-High-Resolution Satellite Imagery

Abstract

Most research pertaining to mapping deprived urban areas is limited to locating and delineating deprived area's extents within and across cities. In this work, we go beyond and characterize deprived areas by utilizing a wide suit of remotely sensed predictors to map the intra-urban distribution of land cover (LC) in deprived communities in Nairobi, Kenya. We assess the contribution of WorldView-3 (WV-3) multispectral and shortwave infrared bands for the task of deprived urban areas land cover classification at a very-high-resolution scale. Our results highlight the potential of WV -3 to accurately map the LC while the potential of intra-urban transferability was shown to be satisfactory. Moreover, feature selection dramatically decreased the computational complexity of the LC models with no losses in classification accuracy. We propose a set of indicators such as the density of garbage piles to be extracted at an aggregated grid level. This aggregation helps characterize urban deprivation at a fine scale and assist local authorities and stakeholders in implementing evidence-based policy making.