B3-CDG: A pseudo-sample diffusion generator for bi-temporal building binary change detection

Building change detection (CD) plays a crucial role in urban planning, land resource management, and disaster monitoring. Currently, deep learning has become a key approach in building CD, but challenges persist. Obtaining large-scale, accurately registered bi-temporal images is difficult, and annotation is time-consuming. Therefore, we propose B³-CDG, a bi-temporal building binary CD pseudo-sample generator based on the principle of latent diffusion. This generator treats building change processes as local semantic states transformations. It utilizes textual instructions and mask prompts to generate specific class changes in designated regions of single-temporal images, creating different temporal images with clear semantic transitions. B³-CDG is driven by large-scale pretrained models and utilizes external adapters to guide the model in learning remote sensing image distributions. To generate seamless building boundaries, B³-CDG adopts a simple and effective approach—dilation masks—to compel the model to learn boundary details. In addition, B³-CDG incorporates diffusion guidance and data augmentation to enhance image realism. In the generation experiments, B³-CDG achieved the best performance with the lowest FID (26.40) and the highest IS (4.60) compared to previous baseline methods (such as Inpaint and IAug). This method effectively addresses challenges such as boundary continuity, shadow generation, and vegetation occlusion while ensuring that the generated building roof structures and colors are realistic and diverse. In the application experiments, B³-CDG improved the IOU of the validation model (SFFNet) by 6.34 % and 7.10 % on the LEVIR and WHUCD datasets, respectively. When the real data is extremely limited (using only 5 % of the original data), the improvement further reaches 33.68 % and 32.40 %. Moreover, B³-CDG can enhance the baseline performance of advanced CD models, such as SNUNet and ChangeFormer. Ablation studies further confirm the effectiveness of the B³-CDG design. This study introduces a novel research paradigm for building CD, potentially advancing the field. Source code and datasets will be available at https://github.com/ABCnutter/B3-CDG.