Geospatial Design of Energy Systems for Africa (GeoDESA) Citizen Science aims to integrate citizen science in rural electrical grid planning for developing regions. As over 800 million people still lack reliable access to electricity, and the UN is targeting affordable clean energy access for all by 2030, innovative electrification strategies are needed to close the gap. Data-driven geospatial system planning can be an effective way to accelerate electrification. High-resolution geographic data and modern computing power can be leveraged to plan community-specific grid development cheaply and quickly, reducing costs by an order of magnitude compared to traditional methods. One key challenge in this approach is the need for detailed home location data. Home locations are necessary to design grid topologies and architectures. This level of specificity enables better grid cost estimates and community-appropriate design. Currently available home location datasets, such as OpenStreetMap, are typically incomplete, particularly in the hardest-to-reach rural poor areas. High-resolution satellite imagery and modern computer vision algorithms can fill the gaps in these datasets. While many algorithms have been trained to detect housing in the past, they are typically based on rich urban contexts. The full diversity of rural and remote housing styles must be taken into account to enable the dwelling detection needed for electrification design. By engaging citizen scientists fluent in local rural housing styles in the labeling of homes in satellite imagery, local knowledge can be incorporated in a scalable data-driven approach. With accurate and context-informed labelled satellite data, computer vision algorithms can be trained to reliably locate rural dwellings in the hardest to reach areas, allowing grids to be efficiently designed to suit community needs. This proposal complements past and ongoing work undertaken at the University of Oxford in rural Africa electrification from the SONG and RELCON projects. The methods developed and data generated will be useful for electrical system planning in many rural and remote contexts, and can cross-apply into other geographic planning disciplines, such as urban planning, migration tracking, and geospatial poverty estimation.