The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

Geographic information generalization underpins multi-scale spatial databases and cartography, and reliable DEM downscaling is critical to maintaining geomorphological consistency across map scales. Traditional DEM simplification and resampling methods rely on local geometric filtering and overlook global terrain structures, frequently causing structural distortions such as broken ridges and deformed slopes. This study proposes DD-GAN, a generative adversarial network constrained by terrain morphological factors for high-fidelity DEM downscaling. Built on a GAN architecture, the model embeds local relief and gradient as physical loss terms to prioritize major geomorphic skeletons and suppress trivial micro-terrain during resolution reduction, avoiding the indiscriminate over-smoothing of conventional sampling approaches. Multi-scale experiments covering downscaling factors ranging from 2× to 5× are conducted using mountainous datasets from Chongqing, Alaska, and Colorado. Quantitative and visual comparisons against raster interpolation, TIN-based simplification, and ordinary CNN show that DD-GAN mitigates terrain structural distortion and better retains elevation extremes and slope features, with more prominent strengths under large downscaling multiples. This physics-constrained deep learning paradigm provides an automated DEM downscaling solution that facilitates multi-scale terrain representation, supporting cartographic production and geomorphometric analysis.

Terrain generalization—the scale-driven abstraction of digital elevation models (DEMs) that retains dominant landforms while filtering trivial microtopography—has long been dominated by rule-based algorithms, whereas deep learning has mainly targeted the inverse task of DEM super-resolution. This study proposes TG-CNN, a feature-fusion convolutional neural network for DEM down-scaling generalization. The network compresses the high-resolution DEM through a max- or average-pooling front end, combines normalized elevation with slope and sine–cosine circularly encoded aspect as complementary input channels, and is trained with a composite elevation–slope–aspect loss. Training, validation, and testing are conducted on geographically independent mountainous areas under a leave-one-area-out scheme, followed by adjustable IDW fusion post-processing. On the test region, classical bilinear interpolation retains the best elevation accuracy, while the standalone TG-CNN does not reach a comparable level; fused accuracy improves monotonically as the IDW share increases, indicating that the fusion benefit originates from the interpolation component. On the ridge–valley structure, however, TG-CNN behaves as an aggressive yet reliable filter: it achieves the highest precision and the smallest positional offset among all compared methods but retains only part of the reference skeleton, leaving its F1-score below the interpolation baseline. TG-CNN thus realizes a genuine, controllable terrain-abstraction effect, yet deep learning does not yet surpass mature interpolation in pixel-level elevation accuracy—a negative result that clarifies current limitations and points toward residual learning and information-preserving architectures.