PD-SEG: Population Disaggregation Using Deep Segmentation Networks For Improved Built Settlement Mask

2023/07/29 by Muhammad Abdul Rahman, Rahman, Muhammad Abdul, Muhammad Ahmad Waseem +7
Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Impact of Light on Environment and Health #Land Use and Ecosystem Services #Remote-Sensing Image Classification

paper · pdf · doi:10.48550/arxiv.2307.16084

openalex publication_date 2023/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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