High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2
Nando Metzger Rodrigo Caye Daudt Devis Tuia Konrad Schindler
Detailed population maps play an important role in diverse fields ranging
from humanitarian action to urban planning. Generating such maps in a timely
and scalable manner presents a challenge, especially in data-scarce regions. To
address it we have developed POPCORN, a population mapping method whose only
inputs are free, globally available satellite images from Sentinel-1 and
Sentinel-2; and a small number of aggregate population counts over coarse
census districts for calibration. Despite the minimal data requirements our
approach surpasses the mapping accuracy of existing schemes, including several
that rely on building footprints derived from high-resolution imagery. E.g., we
were able to produce population maps for Rwanda with 100m GSD based on less
than 400 regional census counts. In Kigali, those maps reach an $R^2$ score of
66% w.r.t. a ground truth reference map, with an average error of only $\pm$10
inhabitants/ha. Conveniently, POPCORN retrieves explicit maps of built-up areas
and of local building occupancy rates, making the mapping process interpretable
and offering additional insights, for instance about the distribution of
built-up, but unpopulated areas, e.g., industrial warehouses. Moreover, we find
that, once trained, the model can be applied repeatedly to track population
changes; and that it can be transferred to geographically similar regions,
e.g., from Uganda to Rwanda). With our work we aim to democratize access to
up-to-date and high-resolution population maps, recognizing that some regions
faced with particularly strong population dynamics may lack the resources for
costly micro-census campaigns.