  To better understand current trends of urban population growth in Sub-Saharan
Africa, high-quality spatiotemporal population estimates are necessary. While
the joint use of remote sensing and deep learning has achieved promising
results for population distribution estimation, most of the current work
focuses on fine-scale spatial predictions derived from single date census,
thereby neglecting temporal analyses. In this work, we focus on evaluating how
deep learning change detection techniques can unravel temporal population
dynamics at short intervals. Since Post-Classification Comparison (PCC) methods
for change detection are known to propagate the error of the individual maps,
we propose an end-to-end population growth mapping method. Specifically, a
ResNet encoder, pretrained on a population mapping task with Sentinel-2 MSI
data, was incorporated into a Siamese network. The Siamese network was trained
at the census level to accurately predict population change. The effectiveness
of the proposed method is demonstrated in Kigali, Rwanda, for the time period
2016-2020, using bi-temporal Sentinel-2 data. Compared to PCC, the Siamese
network greatly reduced errors in population change predictions at the census
level. These results show promise for future remote sensing-based population
growth mapping endeavors.

Computer Vision and Pattern Recognition;Image and Video Processing