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Population Estimates Using Satellite Imagery
This product offers a scalable solution for estimating populations in informal urban settlements using high-resolution satellite imagery, machine learning, and ground-truth validation. By addressing critical gaps left by traditional census methods, it produces granular, actionable data that supports inclusive urban planning, healthcare access, and digital addressing. Designed to be adaptable across geographies, this tool empowers decision-makers with the insights needed for equitable, data-driven governance in rapidly urbanizing contexts.
Data Source: Google
Location: Karachi, Rawalpindi
Timeline: 2023 – present
Areas of focus: Imagery, validation, planning, inclusion, and scalability
Objectives: This product aims to build a scalable model for estimating populations in uncounted areas, validating AI outputs with field data to create accurate, actionable datasets. The goal is to inform public services, enable digital addressing, and support inclusive policymaking.
Our Approach:
Project Story
Site Selection & Prep
Identifying urban locations with data gaps and securing necessary permissions for field and technical work.
Satellite Data Analysis
Enumerators and supervisors were trained across Rawalpindi and Karachi. Field plans and monitoring strategies were finalized to ensure quality and consistency.
Using high-resolution imagery and machine learning to detect structures and estimate population density in informal settlements.
Ground Truth Validation
Deploying local field teams to collect household-level data and verify AI-generated population estimates.
Data Integration & Layering
Merging satellite, AI, and field data into geospatial layers for urban planning and service delivery tools.
Output Development & Reporting
Producing final population estimates, visual dashboards, and actionable insights for partners and decision-makers.
Replicability & Scaling
Packaging the method as a scalable, adaptable product for use in other undercounted urban settings.
