Urban fringe is an active expanding belt, indicating urban-rural interaction processes. Previous studies have attempted to define urban fringe as the transitional area between urban and rural areas, but there is a lack of quantitative analysis of the periphery boundaries. We developed a novel, the Spatial Segmentation Model (SSM), to detect the extent of urban fringe via calculating the share of the built-up land. Within the urban fringe, we statistically compared the number of built-up patches in each direction and described four urban expanding patterns (stable, sprawling, leaping, and mixing patterns) indicated by the empirical analysis. The results show that this model can reliably detect the urban fringe and could reveal urban growth characteristics. We find the spatial territory changes are highly relative with transport infrastructures in Harbin. Meanwhile, the roads density in the urban core are higher than in the urban fringe. Especially for city roads, roads density in the urban core is more than 4 times higher than in the urban fringe. The growth of the urban fringe is closely related to the development of social economies as well as the space policies and development plans designed by governments. Similar to the post-industry cities worldwide, Harbin should take action to address population decline. Effective land-use and suitable urban growth strategies play an important role in alleviating urban shrinkage. Thus, understanding the dynamics, urban expanding patterns, and driving factors in the urban fringe can help us form a basis for future urban development.

Traditional stereo dense image matching (DIM) methods normally predefine a fixed window to compute matching cost, while their performances are limited by the matching window sizes. A large matching window usually achieves robust matching results in weak-textured regions, while it may cause over-smoothness problems in disparity jumps and fine structures. A small window can recover sharp boundaries and fine structures, while it contains high matching uncertainties in weak-textured regions. To address the issue above, we respectively compute matching results with different matching window sizes and then proposes an adaptive fusion method of these matching results so that a better matching result can be generated. The core algorithm designs a Convolutional Neural Network (CNN) to predict the probabilities of large and small windows for each pixel and then refines these probabilities by imposing a global energy function. A compromised solution of the global energy function is utilized by breaking the optimization into sub-optimizations of each pixel in one-dimensional (1D) paths. Finally, the matching results of large and small windows are fused by taking the refined probabilities as weights for more accurate matching. We test our method on aerial image datasets, satellite image datasets, and Middlebury benchmark with different matching cost metrics. Experiments show that our proposed adaptive fusion of multiple-window matching results method has a good transferability across different datasets and outperforms the small windows, the median windows, the large windows, and some state-of-the-art matching window selection methods.

Feature matching between high-resolution satellite stereos plays an important role in satellite image orientation. However, images of changed regions, weak-textured regions and occluded regions may generate low-quality matches or even mismatches. Furthermore, matching throughout the entire satellite images often has extremely high time cost. To compute good matching results at low time cost, this paper proposes an image block selection method for high-resolution satellite stereos, which processes feature matching in several optimal blocks instead of the entire images. The core of the method is to formulate the block selection into the optimization of an energy function, and a greedy strategy is designed to compute an approximate solution. The experimental comparisons on various satellite stereos show that the proposed method could achieve similar matching accuracy and much lower time cost when compared with some state-of-the-art satellite image matching methods. Thus, the proposed method is a good compromise between matching accuracy and matching time, which has great potential in large-scale satellite applications.

Traditional models that employ CNNs as encoders do not sufficiently combine high-level features and low-level features. However, high-level features are rich in semantic information but lack spatial detail, while low-level features are the opposite. Therefore, the integrated utilization of multi-level features and the bridging of the gap between them is crucial to promote the accuracy of semantic segmentation. To address this issue, we presented communicating mutual attention (CMA) and communicating self-attention (CSA) modules to enhance the interaction and fusion of different levels of feature maps. On the one hand, CMA aggregates the global context information of high-level features into low-level features and embeds the spatial detail localization characteristics of low-level features in high-level features. On the other hand, the CSA module is deployed to integrate the spatially detailed representation of low-level features into the attention map of high-level features. We have experimented with the communicating attention network (CANet), a U-net-like network composed of multiple CMA and CSA modules, on the ISPRS Vaihingen and Potsdam datasets with mean F1-scores of 89.61% and 92.60%, respectively. The results demonstrate that CANet embodies superior performance in the semantic segmentation task of remote sensing of images.

Semantic change detection (SCD) focuses on identifying changes in surface coverage while simultaneously classifying the types of changes. This approach provides detailed information valuable for urban planning, environmental monitoring, and other applications, making it a key area of interest in remote sensing research. Despite recent advances, existing SCD studies are hindered by the lack of high-resolution satellite imagery datasets and insufficiently comprehensive semantic label coverage in publicly available datasets. To address these limitations, we have developed a large-scale high-resolution remote sensing dataset consisting of 11,587 satellite image pairs, each with 1-meter spatial resolution and a size of 512 × 512 pixels, representing land cover changes across Beijing between 2017 and 2018. This dataset encompasses diverse land surface scenes with comprehensive semantic annotations. Furthermore, it includes full-coverage semantic segmentation labels from pre-change phases and a larger sample size of 2048 × 2048 pixels to support future research on multi-class and large-format change detection. We benchmark eight state-of-the-art SCD algorithms using this dataset, providing critical performance metrics that serve as valuable references for subsequent research. This dataset not only addresses existing gaps but also establishes a robust foundation for advancing deep learning-based semantic change detection, enabling more accurate and comprehensive analysis of complex and diverse land cover changes. More information about the project can be found at https://github.com/17x-osborn/HRMS-SCD.