Urban Poverty Mapping Using Remote Sensing and Socioeconomic Indicators in Maiduguri

Maiduguri, the capital of Borno State and the urban epicentre of the North East Nigeria humanitarian crisis, has experienced a dramatic influx of internally displaced persons and significant urban poverty intensification that demands spatially granular poverty mapping to guide targeted service delivery and reconstruction investments. This study produces an urban poverty map for Maiduguri using an integrated remote sensing and socioeconomic indicator approach. Morphological indicators of poverty including roof material type, dwelling footprint area, plot irregularity, and road surface type were extracted from Pleiades very high resolution satellite imagery using machine learning-based classification. These physical indicators were calibrated against household poverty scores from the 2023 Borno State Multidimensional Poverty Assessment, which covered 600 households across 30 wards of Maiduguri and Jere LGAs. Correlation and regression analysis linked image-derived morphological scores to survey-based poverty estimates to produce a ward-level poverty surface. Spatial autocorrelation analysis identified poverty clusters and outlier wards. Results reveal a highly uneven poverty distribution, with the most extreme poverty clusters located in Bulumkutu, Gamboru, and Old Maiduguri wards, which also have the highest concentrations of internally displaced person settlements. Physical morphology indices accurately predict poverty quintile membership with 74 percent accuracy, confirming the value of satellite-based rapid poverty estimation between survey years. The study recommends annual satellite-based poverty map updates as a monitoring tool for Borno State's reconstruction programme and humanitarian resource allocation. Keywords: urban poverty mapping, remote sensing, Maiduguri, Borno State, North East Nigeria.