While it is important that local poverty has been targeted to help predictionand its policies have been established in
emerging economies, this paper assesses the potential of features of local satellite images of high resolution that accurately
presents poverty and economic well-being, with a combination of convolutional neural network (CNN). The properties of items
and clothing are disbursed from Nigerian satellite images, which are used to assess poverty rates in Abuja and other regions. It
includes the properties and density of buildings, shadow areas, which are the type of building height, spread, number of cars,
density and length of roads, agricultural land and roofing materials. Buildings, shadows and road features have a strong
relationship with poverty
