Imago - making satellite data useful, usable and used
Year: 2026
Type: article
Abstract: Satellite observations provide unparalleled insight into spatio-temporal processes on our planet, and are extensively used by many communities. However, their use in the social sciences and policymaking has historically been limited by technical, computational, and methodological barriers. Imago, a consortium of research software engineers, data scientists and social/policy experts addresses this gap, transforming terabytes of complex Earth Observation data into digestible, actionable insights for social scientists and policymakers. RSEs are embedded throughout the project, shaping not only the design of the software and technical infrastructure but also the underlying scientific questions. Imago comprises several data products, which focus on key knowledge gaps in these communities. We are developing an extensive library of training and workshops to upskill non-experts in how best to use and interpret our data, ensuring that our outputs are usable and reusable beyond the project itself. Central to Imago is the ongoing development of imago, an open-source Python library designed to streamline processing workflows; parallelising and optimising the process of obtaining satellite data and the key processing steps needed to transform raw pixels into actionable insights. We demonstrate this approach through the Sun Probability Framework (SPF), a novel data product that converts underutilised satellite cloud mask data into meaningful measures of sunlight exposure. SPF combines scalable, parallel processing via the imago library with new algorithms designed to quantify and correct systematic biases. The result is a rigorously validated, reproducible software pipeline, the results of which are ready for immediate use in social research.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204851127