The change detection of high-resolution images plays an important role in practical applications. However, most existing studies use local or global attention mechanisms alone to filter and screen for changing features. In this study, we proposed a triple attention multiscale fusion network (TAMFNet) that can effectively utilize both global and local attention mechanisms, thereby improving the ability to detect the location of change areas and fully outline the change areas. First, we employed a fully convolutional network to extract features from dual temporal images at different scales. Second, three complementary attention mechanisms, namely, the spatial attention mechanism (SAM), channel attention mechanism (CAM), and the multihead self-attention (MSA) module, were integrated to extract and fuse global and local features. Finally, to address semantic and scale differences, we utilized the cross scale fusion (CSF) module, pyramid pooling module (PPM), and pyramid receptive field (PRF) module to aggregate features from adjacent scales for comprehensive feature transmission. To demonstrate the effectiveness of our method, we tested it on the LEVIR-CD and WHU-CD datasets. The results showed that our model achieved intersection over union (IOU) scores of 80.17% and 77.23% on the datasets, outperforming comparative models. Ablation experiments on the LEVIR-CD dataset confirmed the positive impact of each intermediate module in TAMFNet, with an overall 2.35% increase in IOU score.

Dunhuang mural inpainting aims to fill in the missing regions of damaged murals with realistic content. Denoising probabilistic diffusion model (DDPM) has made great strides in semantic generation and shown promising results in image inpainting. However, three potential challenges prevent existing diffusion-based methods from restoring the Dunhuang murals: 1) effective visual information cannot be accurately extracted due to historical reasons, with most of the pixels being faded; 2) there are semantic discrepancy between damaged and visible regions in the inpainting results; and 3) the original structure and style of the damaged regions cannot be adequately restored. To this end, we propose a novel adversarial diffusion model for mural inpainting, which consists of: 1) a mural enhancement module named pixel-enhanced fire-controlled pulse-coupled neural network (PEFCPCNN), designed to enhance faded pixels to accurately extract the visual features of the mural; 2) a novel adversarial diffusion framework that optimizes the sampling prediction of mural over time steps; and 3) line drawing and different loss functions to constrain the reconstructed content to approximate the structure and style of original mural. The variational transform layer (VTL) and multi-scale contextual feature aggregation (MCFA) module are proposed to reconstruct content that is structurally coherent and texturally reasonable. Experiments on the Dunhuang mural dataset demonstrate that the proposed method outperforms state-of-the-art methods in terms of both the semantic reasonableness and global semantic consistency of inpainting content.

To evaluate the urban ecosystem condition quantitatively is still a big issue remaining to be solved. This contribution developed a quantitative method, called Ecoservice Radiance Model (ERM), to evaluate ecological condition in built area. As the evaluation index, ecoservice which was provided by eco-land was considered distance attenuated and can be radiated to the around built area according to distance. Through a case study in Bujiriver basin in Shenzhen, China, it could be concluded that the ecoservice can be allocated to built area efficiently and the allocation result can reflect the role of small eco-land in improving the ecological condition of built area.