BANet: A bilateral attention network for extracting changed buildings between remote sensing imagery and cadastral maps

• Published in: International journal of applied earth observation and geoinformation Vol. 139; p. 104486
• Main Authors: Li, Qingyu, Mou, Lichao, Shi, Yilei, Zhu, Xiao Xiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2025; Elsevier
• Subjects: Bilateral attention; Building change detection; Cadastral map; Deep learning; Remote sensing imagery; Deep learning; Bilateral attention; Building change detection; Cadastral map; Remote sensing imagery
• ISSN: 1569-8432
• DOI: 10.1016/j.jag.2025.104486

Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works focus on extracting all buildings from remote sensing images, we argue that these methods not only disregard preliminary information on cadastral maps but also fail to preserve building priors in unchanged areas on cadastral maps. Therefore, we focus on the task of extracting changed buildings (i.e., newly built and demolished buildings) from remote sensing images and cadastral maps. To address this task, we create an image-map building change detection (IMBCD) dataset, formed by around 27K pairs of remote sensing images and maps and their corresponding changed buildings in six distinct geographical areas across the globe. Accordingly, we propose a Bilateral Attention Network (BANet), introducing a novel attention mechanism: changed-first (CF) attention and non-changed-first (NCF) attention. This bilateral attention mechanism helps to refine the uncertain areas between changed and non-changed regions. Extensive experiments on our IMBCD dataset showcase the superior performance of BANet. Specifically, our BANet outperforms state-of-the-art models with F1 scores of 90.00% and 63.00% for the IMBCD-WHU and IMBCD-Inria datasets. This confirms that the leverage of bilateral attention blocks (BAB) can boost performance. •We extract changed buildings between remote sensing images and cadastral maps.•We propose a dataset, IMBCD, for building change detection.•We propose a novel network, BANet, for building change detection.