PD-SEG: Population Disaggregation Using Deep Segmentation Networks For Improved Built Settlement Mask
This work addresses the need for accurate population statistics for policy-making and resource planning in developing countries, representing an incremental improvement over existing datasets like WorldPop and Meta.
The paper tackled the problem of inaccurate population density estimates in developing nations like Pakistan by using deep segmentation networks and satellite imagery to create an improved built settlement mask, achieving precise population counts at a 30m x 30m resolution.
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.