  To better understand the dynamics of human settlements, thorough knowledge of
the uncertainty in geospatial built-up surface datasets is critical. While
frameworks for localized accuracy assessments of categorical gridded data have
been proposed to account for the spatial non-stationarity of classification
accuracy, such approaches have not been applied to (binary) built-up land data.
Such data differs from other data such as land cover data, due to considerable
variations of built-up surface density across the rural-urban continuum
resulting in switches of class imbalance, causing sparsely populated confusion
matrices based on small underlying sample sizes. In this paper, we aim to fill
this gap by testing common agreement measures for their suitability and
plausibility to measure the localized accuracy of built-up surface data. We
examine the sensitivity of localized accuracy to the assessment support, as
well as to the unit of analysis, and analyze the relationships between local
accuracy and density / structure-related properties of built-up areas, across
rural-urban trajectories and over time. Our experiments are based on the
multi-temporal Global Human Settlement Layer (GHSL) and a reference database
for the state of Massachusetts (USA). We find strong variation of suitability
among commonly used agreement measures, and varying levels of sensitivity to
the assessment support. We then apply our framework to assess localized GHSL
data accuracy over time from 1975 to 2014. Besides increasing accuracy along
the rural-urban gradient, we find that accuracy generally increases over time,
mainly driven by peri-urban densification processes in our study area.
Moreover, we find that localized densification measures derived from the GHSL
tend to overestimate peri-urban densification processes that occurred between
1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.
