Satellite altimetry technology has unparalleled advantages in the monitoring of hydrological resources. After decades of development, satellite altimetry technology has achieved a perfect integration from the geometric research of geodesy to the natural resource monitoring research. Satellite altimetry technology has shown great potential, whether solid or liquid. In general, this paper systematically reviews the development of satellite altimetry technology, especially in terms of data availability and program practicability, and proposes a multi-source altimetry data fusion method based on deep learning. Secondly, in view of the development prospects of satellite altimetry technology, the challenges and opportunities in the monitoring application and expansion of surface water changes are sorted out. Among them, the limitations of the data and the redundancy of the program are emphasized. Finally, the fusion scheme of altimetry technology and deep learning proposed in this paper is presented. It is hoped that it can provide effective technical support for the monitoring and application research of hydrological resources.

Building clustering is a key challenge in cartographic generalization, where the goal is to group spatially related buildings into semantically coherent clusters while preserving the true distribution patterns of urban structures. Existing methods often rely on either spatial distance or building feature similarity alone, leading to clusters that sacrifice either accuracy or spatial continuity. Moreover, most deep learning-based approaches, including graph attention networks (GATs), fail to explicitly incorporate spatial distance constraints and typically restrict message passing to first-order neighborhoods, limiting their ability to capture long-range structural dependencies. To address these issues, this paper proposes LA-GATs, a multi-feature constrained and spatially adaptive building clustering network. First, a Delaunay triangulation is constructed based on nearest-neighbor distances to represent spatial topology, and a heterogeneous feature matrix is built by integrating architectural spatial features, including compactness, orientation, color, and height. Then, a spatial distance-constrained attention mechanism is designed, where attention weights are adjusted using a distance decay function to enhance local spatial correlation. A second-order neighborhood aggregation strategy is further introduced to extend message propagation and mitigate the impact of triangulation errors. Finally, spectral clustering is performed on the learned similarity matrix. Comprehensive experimental validation on real-world datasets from Xi’an and Beijing, showing that LA-GATs outperforms existing clustering methods in both compactness, silhouette coefficient and adjusted rand index, with up to about 21% improvement in residential clustering accuracy.

On 21 May 2021, an Ms 6.4 earthquake struck Yangbi County, Dali Prefecture, Yunnan Province, China, which is the largest earthquake to hit this area since 1976. In this paper, we obtained the coseismic deformation of the Yangbi earthquake in Yunnan Province based on the interferometric synthetic aperture radar (InSAR) observation. After that, we obtained the fault geometry and slip distribution model of this earthquake via the two-step inversion method. The maximum deformation in the ascending orbit along the line of sight (LOS) direction was 7.3 cm, and the maximum deformation in the descending orbit along the LOS direction was 8.9 cm; the slip distribution model showed that the slip distribution of this earthquake was concentrated at a depth of 1–14 km, and the maximum slip was 0.6 m at a depth of 5 km. Based on the modeling result, it was inferred that the seismogenic fault of this earthquake was a dextral strike-slip fault on the west side of the Weixi-Qiaohou–Weishan fault. Combining the existing geological data and the changes in Coulomb stress caused by this earthquake, the seismic hazard and seismogenic structure in the area near the epicenter were analyzed and discussed, and the results showed that, in the northwest of the Weixi-Qiaohou fault zone, there will be an increased hazard of a future earthquake in the NW trend; thus, we should pay attention to this area.