  The level of landscape heterogeneity may affect the performance of remote
sensing based land use / land cover classification. However, the relationship
between mapping accuracy of built-up surfaces and morphological characteristics
of built-up areas has not been analyzed explicitly, and previous studies
typically rely on aggregated landscape metrics to quantify the morphology of
built-up areas, neglecting the fine-grained spatial variation and scale
dependency of such metrics. Herein, we aim to fill this gap by assessing the
associations between focal landscape metrics, derived from binary built-up
surfaces, and focal data accuracy estimates. We test our approach for built-up
surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA),
by examining the explanatory power of landscape metrics for predictive modeling
of commission and omission errors in the GHS-BUILT R2018A data product. We find
that the Landscape Shape Index (LSI) exhibits the highest levels of correlation
to focal accuracy measures. These relationships are scale-dependent, and
increase with the level of spatial support. Our results are consistent across
different regions within the U.S., and we find that the Recall measure has the
strongest relationship to measures of built-up surface morphology across
different temporal epochs and spatial resolutions. Regression analysis results
(R2>0.9) indicate that it is possible to estimate commission errors in the GHSL
in the absence of reference data, and that omission errors in the GHSL can be
modeled without accessing the data themselves. Lastly, we test the
generalizability of our predictive accuracy models to a different version of
the GHSL (i.e., the GHS-BUILT-S2) covering a study area in North Carolina. We
find varying levels of model transferability that increases with the spatial
support at which landscape metrics and accuracy estimates are calculated.
