ArXiv Preprint
Fine-grained population maps are needed in several domains, like urban
planning, environmental monitoring, public health, and humanitarian operations.
Unfortunately, in many countries only aggregate census counts over large
spatial units are collected, moreover, these are not always up-to-date. We
present POMELO, a deep learning model that employs coarse census counts and
open geodata to estimate fine-grained population maps with 100m ground sampling
distance. Moreover, the model can also estimate population numbers when no
census counts at all are available, by generalizing across countries. In a
series of experiments for several countries in sub-Saharan Africa, the maps
produced with POMELOare in good agreement with the most detailed available
reference counts: disaggregation of coarse census counts reaches R2 values of
85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.
Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler, Devis Tuia
2022-11-08
