The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.

Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep learning solutions, particularly convolutional neural networks (CNNs), represent the state of the art (SOTA) for CD. However, CNN-based models require substantial amounts of annotated data, which can be both expensive and time-consuming. Conversely, acquiring a large volume of unannotated images is relatively easy. Recently, self-supervised contrastive learning has emerged as a promising method for learning from unannotated images, thereby reducing the need for annotation. However, most existing methods employ random values or ImageNet pre-trained models to initialize their encoders and lack prior knowledge tailored to the demands of CD tasks, thus constraining the performance of CD models. To address these challenges, we introduce a novel feature-differencing-based framework called Barlow Twins for self-supervised pre-training and fine-tuning in CD (BTCD). The proposed approach employs absolute feature differences to directly learn unique representations associated with regions that have changed from unlabeled bi-temporal remote sensing images in a self-supervised manner. Moreover, we introduce invariant prediction loss and change consistency regularization loss to enhance image alignment between bi-temporal images in both the decision and feature space during network training, thereby mitigating the impact of variation in radiation conditions, noise, and imaging viewpoints. We select the improved UNet++ model for fine-tuning self-supervised pre-training models and conduct experiments using two publicly available LULC CD datasets. The experimental results demonstrate that our proposed approach outperforms existing SOTA methods in terms of competitive quantitative and qualitative performance metrics.