"Identifying poverty, and the regions that are most in need is a key component in being able to tackle the problem of poverty. Satellite imagery is helping researchers do just this. An abundance of images taken by satellites on a constant stream can lend a hand to identifying global activities that reflect poor and rich regions - areas with a high density of light at night are typically wealthier than those in darkness, with little or no access to electricity over nighttime periods."
"Stanford University’s Department of Earth System Science used satellite images of areas in daylight in research in order to ‘fill in the gaps’ of nighttime images alone in identifying the most poverty-stricken regions in parts of Africa. Feeding an algorithm with both night and day satellite images of Rwanda, Nigeria, Uganda, Malawi, and Tanzania, they were able to identify signifiers in the daytime pictures. Combining both image sets, this helped the computer predict poverty in these regions. When compared with survey data obtained from households within them - this led Burke and his team to be able to predict poverty with an 81-99% accuracy as compared to the night time satellite images alone. AI, in this case, can not only help with the identification of areas most in need of aid, but also help organisations and aid workers on the ground in locations to measure how effective their efforts are in combating poverty."
Improved capability to predict poverty in an area - 81-99% accuracy compared to previous methodology.