PD-SEG: Population Disaggregation Using Deep Segmentation Networks For
  Improved Built Settlement Mask
Muhammad Abdul Rahman Muhammad Ahmad Waseem Zubair Khalid Muhammad Tahir Momin Uppal
Any policy-level decision-making procedure and academic research involving
the optimum use of resources for development and planning initiatives depends
on accurate population density statistics. The current cutting-edge datasets
offered by WorldPop and Meta do not succeed in achieving this aim for
developing nations like Pakistan; the inputs to their algorithms provide flawed
estimates that fail to capture the spatial and land-use dynamics. In order to
precisely estimate population counts at a resolution of 30 meters by 30 meters,
we use an accurate built settlement mask obtained using deep segmentation
networks and satellite imagery. The Points of Interest (POI) data is also used
to exclude non-residential areas.