Change detection (CD) is one of the important applications of remote sensing and plays an important role in disaster assessment, land use detection, and urban sprawl tracking. High-accuracy fully supervised methods are the main methods for CD tasks at present. However, these methods require a large amount of labeled data consisting of bi-temporal images and their change maps. Moreover, creating change maps takes a lot of labor and time. To address this limitation, a simple semi-supervised change detection method based on consistency regularization and strong augmentation is proposed in this paper. First, we construct a Siamese nested UNet with graph attention mechanism (SANet) and pre-train it with a small amount of labeled data. Then, we feed the unlabeled data into the pre-trained SANet and confidence threshold filter to obtain pseudo-labels with high confidence. At the same time, we produce distorted images by performing strong augmentation on unlabeled data. The model is trained to make the CD results of the distorted images consistent with the corresponding pseudo-label. Extensive experiments are conducted on two high-resolution remote sensing datasets. The results demonstrate that our method can effectively improve the performance of change detection under insufficient labels. Our methods can increase the IoU by more than 25% compared to the state-of-the-art methods.

Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but struggle to model long-range dependencies across facets effectively. Furthermore, owing to high computational complexity or excessive pre-processing operations, these methods lack the capability for the efficient semantic segmentation of large-scale urban meshes. Inspired by Mamba, we propose MambaMeshSeg-Net, a novel 3D urban mesh semantic segmentation method based on the State Space Model (SSM). The proposed method incorporates a hybrid scanning strategy that adaptively scans 3D urban meshes to extract long-range dependencies across facets, enhancing semantic segmentation performance. Moreover, our model exhibits faster performance in both inference and pre-processing compared to other mainstream models. In comparison with existing state-of-the-art (SOTA) methods, our model demonstrates superior performance on two widely utilized open urban mesh datasets.