  Semantic segmentation techniques for extracting building footprints from
high-resolution remote sensing images have been widely used in many fields such
as urban planning. However, large-scale building extraction demands higher
diversity in training samples. In this paper, we construct a Global Building
Semantic Segmentation (GBSS) dataset (The dataset will be released), which
comprises 116.9k pairs of samples (about 742k buildings) from six continents.
There are significant variations of building samples in terms of size and
style, so the dataset can be a more challenging benchmark for evaluating the
generalization and robustness of building semantic segmentation models. We
validated through quantitative and qualitative comparisons between different
datasets, and further confirmed the potential application in the field of
transfer learning by conducting experiments on subsets.
