A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction

TL;DR

This paper introduces a large, diverse building dataset called WHU-Mix and a plug-and-play domain generalization method, BSM, to improve building extraction from remote sensing images, achieving significant performance gains.

Contribution

The paper presents the WHU-Mix dataset for better diversity and quality, and proposes BSM, a novel domain generalization module, to enhance model robustness and generalization.

Findings

WHU-Mix dataset improves building extraction mIoU by 6-36%.

Inaccurate labels can decrease IoU by about 20%.

BSM module outperforms baseline and recent methods in generalization.

Abstract

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.