High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2

TL;DR

POPCORN is a scalable, minimal-data method for generating high-resolution population maps using Sentinel satellite imagery and coarse census data, outperforming existing approaches and providing interpretable insights.

Contribution

The paper introduces POPCORN, a novel population mapping approach that requires only free satellite images and coarse census counts, achieving higher accuracy and interpretability than existing methods.

Findings

Achieved 66% R^2 in Kigali population maps.

Produced accurate maps with less than 400 census counts for Rwanda.

Enabled transferability of models between similar regions.

Abstract

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 about 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.