ABIBE: Adaptive Building Information-Based Extraction From Remote Sensing Imagery Using Vision–Language Models

Building extraction from remote sensing imagery is essential for urban planning, population monitoring, and emergency response. However, traditional methods often struggle with accurate building delineation due to complex architectural features and varying imaging conditions. In this article, we propose an adaptive building information-based extraction (ABIBE) framework that leverages vision–language models (VLMs) for high-precision building extraction from remote sensing imagery. Our approach introduces two key innovations: 1) a hierarchical feature transfer mechanism that selectively extracts and adapts visual representations from the JanusPro VLM and 2) a dynamic multiscale feature fusion technique that adaptively integrates these features with spatial information through cross-modal attention mechanisms. Extensive experiments on the WHU Building Dataset demonstrate that our method significantly outperforms state-of-the-art approaches, achieving 91.39% IoU and 95.50% $F1$ -score. We also provide comprehensive performance analysis, including computational complexity, runtime efficiency, and resource requirements, to evaluate the practical applicability of the proposed framework. The ABIBE framework provides a promising solution for high-precision building extraction from remote sensing imagery, with particular advantages in areas with complex architectural structures and high building density.