  In this paper, we introduce a new building dataset and propose a novel domain
generalization method to facilitate the development of building extraction from
high-resolution remote sensing images. The problem with the current building
datasets involves that they lack diversity, the quality of the labels is
unsatisfactory, and they are hardly used to train a building extraction model
with good generalization ability, so as to properly evaluate the real
performance of a model in practical scenes. To address these issues, we built a
diverse, large-scale, and high-quality building dataset named the WHU-Mix
building dataset, which is more practice-oriented. The WHU-Mix building dataset
consists of a training/validation set containing 43,727 diverse images
collected from all over the world, and a test set containing 8402 images from
five other cities on five continents. In addition, to further improve the
generalization ability of a building extraction model, we propose a domain
generalization method named batch style mixing (BSM), which can be embedded as
an efficient plug-and-play module in the frond-end of a building extraction
model, providing the model with a progressively larger data distribution to
learn data-invariant knowledge. The experiments conducted in this study
confirmed the potential of the WHU-Mix building dataset to improve the
performance of a building extraction model, resulting in a 6-36% improvement in
mIoU, compared to the other existing datasets. The adverse impact of the
inaccurate labels in the other datasets can cause about 20% IoU decrease. The
experiments also confirmed the high performance of the proposed BSM module in
enhancing the generalization ability and robustness of a model, exceeding the
baseline model without domain generalization by 13% and the recent domain
generalization methods by 4-15% in mIoU.
