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Building change detection involves identifying areas where buildings have changed by comparing multi-temporal remote sensing imagery of the same geographical region. Recent advances in Transformer-based methods have significantly improved remote sensing change detection. However, current Transformer models still exhibit persistent limitations in effectively extracting multi-scale semantic features within complex scenarios. To more effectively extract multi-scale semantic features in complex scenes, we propose a novel model, which is the Transformer-based Multi-Scale Semantic Extraction Change Detection Network (MSSE-CDNet). The model employs a Siamese network architecture to enable precise change recognition. MSSE-CDNet comprises four parts, which together contain five modules: (1) a CNN feature extraction module, (2) a multi-scale semantic extraction module, (3) a Transformer encoder and decoder module, and (4) a prediction module. Comprehensive experiments on the standard LEVIR-CD benchmark for building change detection demonstrate our approach’s superiority over state-of-the-art methods. Compared to existing models such as FC-Siam-Di, FC-Siam-Conc, DTCTSCN, BIT, and SNUNet, MSSE-CDNet achieves significant and consistent gains in performance metrics, with F1 scores improved by 4.22%, 6.84%, 2.86%, 1.22%, and 2.37%, respectively, and Intersection over Union (IoU) improved by 6.78%, 10.74%, 4.65%, 2.02%, and 3.87%, respectively. These results robustly substantiate the effectiveness of our framework on an established benchmark dataset.

Due to rapid urbanization and the continuous increase in building stock, significant challenges arise for historic district preservation. To overcome the persistent challenge of insufficient small-scale unauthorized structure detection in dense historic districts—a critical limitation of existing deep learning-based change detection frameworks—this paper introduces a Siamese network integrated with a lightweight visual transformer, effectively resolving subtle change omission in complex scenarios. The model utilizes context-aware local enhancement to capture high-frequency local information, significantly improving its accuracy in identifying changed regions. Within the change detection network, a CNN feature extractor first performs downsampling on the input image pair to preliminarily extract feature information. Subsequently, a semantic extraction module extracts and enhances semantic information from the feature maps. Finally, a prediction module calculates the differences between the features of the two images and generates the change prediction results. The reasrech comprehensively validated the model on the public LEVIR-CD dataset. Experimental results demonstrate significant improvements in performance metrics compared to other models. The findings indicate that the improved model also performs excellently on this dataset, verifying its effectiveness and robustness, and showcasing its ability to substantially reduce both omissions and false detections. This study offers a solution for high-accuracy remote sensing change detection by improving deep learning-based models.