Mapping Nigeria’s Poverty with Satellite Imagery Using Deep Learning: A Comparative Study of Day and Night Light Satellite Images

Abstract

Abstract: Poverty mapping plays a crucial role in understanding and addressing socioeconomic disparities within a country. Traditional methods of poverty estimation often rely on survey data, which can be time consuming, expensive, and limited in coverage. In recent years, the advent of satellite imagery and deep learning techniques has opened up new avenues for poverty mapping. This study aims to compare the effectiveness of day and night light satellite imagery in mapping poverty in Nigeria using deep learning models. In this thesis, daylight satellite data is used to directly forecast poverty by evaluating Convolutional Neural Network (CNN) models. one methods semantic segmentation are put forth and contrasted with the multistep learning method that makes use of an image of nighttime lights. We conduct our experiments with satellite pictures from Google Maps and NOAA that are publicly available throughout the day and at night. Combining the night lights data with the semantic segmentation approach to yields the best model