A Multi-Task Deep Learning Model for Population and LULC (M2PL-NET) Prediction with Scaling to a People Flow Grid

This study attempts to create a comprehensive understanding of a regional population's residence and movements at high spatio-temporal resolution. Most approaches to estimating people flow focus purely on mobile GPS data, but this represents a relatively small and imbalanced user distribution across geographical regions. Hence, this paper proposes a new approach to address these issues by combining a multi-task deep learning satellite imagery technique with user GPS trajectories to predict dynamic population. Static population results demonstrate that the multi-task deep learning model performs reasonably well on the unseen data with Mean Absolute Error (MAE) of 3.15. Night-time predicted population was most highly correlated to observed static population, depicting the efficacy of the people flow grid.