Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.

ABSTRACT: Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore

SUBMITTER: Uhl JH 

PROVIDER: S-EPMC10653213 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature