  High-resolution human settlement maps provide detailed delineations of where
people live and are vital for scientific and practical purposes, such as rapid
disaster response, allocation of humanitarian resources, and international
development. The increased availability of high-resolution satellite imagery,
combined with powerful techniques from machine learning and artificial
intelligence, has spurred the creation of a wealth of settlement datasets.
However, the precise agreement and alignment between these datasets is not
known. Here we quantify the overlap of high-resolution settlement map for 42
African countries developed by Google (Open Buildings), Meta (High Resolution
Population Maps) and GRID3 (Geo-Referenced Infrastructure and Demographic Data
for Development). Across all studied countries we find large disagreement
between datasets on how much area is considered settled. We demonstrate that
there are considerable geographic and socio-economic factors at play and build
a machine learning model to predict for which areas datasets disagree. It it
vital to understand the shortcomings of AI derived high-resolution settlement
layers as international organizations, governments, and NGOs are already
experimenting with incorporating these into programmatic work. As such, we
anticipate our work to be a starting point for more critical and detailed
analyses of AI derived datasets for humanitarian, planning, policy, and
scientific purposes.
