Being able to project future spatial population distributions is an important tool to tackle challenges following continuous global population growth and possible future climate challenges. This report presents an answer to how a convolutional neural network can be used to project future spatial population distribution and what results can be achieved by using this approach. This is investigated by designing a convolutional neural network, PopNet. PopNet identifies complex spatial patterns in historical data, on a 250 meter resolution grid and projects the future spatial population distribution. From this architecture, two future scenarios are simulated based on IIASA SSP2 population projections for Denmark and France respectively and the results evaluated. While the neural network method does have flaws, the results prove how, and that, convolutional neural networks can be used to project future population distribution. A number of key challenges, strengths and weaknesses are found and further alterations are proposed that could improve PopNet precision and applicability for future use.