Semi-Supervised Building Footprint Extraction Using Debiased Pseudo-Labels

Accurate extraction of building footprints from satellite imagery is of high value. Currently, deep learning methods are predominant in this field due to their powerful representation capabilities. However, they generally require extensive pixel-wise annotations, which constrains their practical application. Semi-supervised learning (SSL) significantly mitigates this requirement by leveraging large volumes of unlabeled data for model self-training (ST), thus enhancing the viability of building footprint extraction. Despite its advantages, SSL faces a critical challenge: the imbalanced distribution between the majority background class and the minority building class, which often results in model bias toward the background during training. To address this issue, this article introduces a novel method called DeBiased matching (DBMatch) for semi-supervised building footprint extraction. DBMatch comprises three main components: 1) a basic supervised learning module (SUP) that uses labeled data for initial model training; 2) a classical weak-to-strong ST module that generates pseudo-labels from unlabeled data for further model ST; and 3) a novel logit debiasing (LDB) module that calculates a global logit bias between building and background, allowing for dynamic pseudo-label calibration. To verify the effectiveness of the proposed DBMatch, extensive experiments are performed on three public building footprint extraction datasets covering six global cities in SSL setting. The experimental results demonstrate that our method significantly outperforms some advanced SSL methods in semi-supervised building footprint extraction. Our codes will be publicly provided at https://github.com/zhu-xlab/SSL_Buildings.