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“To implement effective rural electricity access systems, it is fundamental to identify where potential consumers live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thousands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of 7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to 69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67% and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for accelerated rural home-level mapping to enable energy system design.”
To implement effective rural electricity access systems, it is fundamental to identify where potential consumers live. This article tests whether citizen science, satellite imagery, and computer vision can be combined to map remote off-grid homes.
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Thousands of citizen scientists generated over half a million annotations in a project which could help with future energy system design and to train automated computer mapping.
Alycia Leonard, Scot Wheeler, and Malcolm McCulloch (Department of Engineering Science, University of Oxford).
This material has been produced in part under the Climate Compatible Growth programme
Leanard, A., Wheeler, S., and McCulloch, M. (2022) Power to the people: Applying citizen science and computer vision to home mapping for rural energy access. International Journal of Applied Earth Observation and Geoinformation, Vol 108, April 2022, 102748.