Accurate population data is essential for activities ranging from humanitarian response to climate adaptation, but accessing reliable and up-to-date information is often challenging. In these data-scarce settings, high-resolution population estimates can be produced using statistical models that combine geolocated survey data with geospatial datasets. WorldPop works to fill this gap by producing high-resolution, open-access population datasets. Based at the University of Southampton, the research group integrates census data, satellite imagery, and statistical and machine learning models to generate global population estimates by age and sex at a 100-meter spatial resolution. These datasets address major gaps in traditional demographic data, which are often outdated, incomplete, or unavailable especially in low- and middle-income countries. The resulting spatial databases are applied across multiple sectors, including disease modelling, disaster risk assessment, resource allocation, poverty mapping, and urban and environmental planning. By mapping healthcare access and population movements, WorldPop enables targeted interventions for vaccination, epidemic control and humanitarian response. A key component of the initiative involves collaboration with national statistical agencies, UN agencies and health organizations to build local analytical capacity and co-develop sustainable data systems. The project’s commitment to open data, transparent methodologies, and continuous user feedback ensures that outputs are reproducible and continuously improved. Through these efforts, WorldPop contributes to global goals of inclusive development by ensuring that every person, everywhere, is represented in population data.