Microlandform classification of grid digital elevation models (DEMs) is the foundation of digital landform refinement applications. To solve the shortcomings of the traditional regular grid DEM microlandform classification method, including low automation and incomplete classification results, a support vector machine (SVM) classifier was designed for grid DEM microlandform classification, and an automatic grid-based DEM microlandform classification method based on the SVM method was created. The experiment applies the SVM-based grid DEM microlandform classification method to identify different hill positions, namely, the summit, shoulder, back-slope, foot-slope, toe-slope, and alluvium. The results show that this method is most efficient in identifying the toe-slope, with an accuracy rate of 99.60%, and least efficient in identifying the foot-slope, with an accuracy rate of 98.18%. The kappa coefficient and model evaluation index F1-score verify that the method and model are reliable when applied to grid DEM microlandform classification problems.

Reliable localization is essential for the efficiency and safety of autonomous underwater operations. In this study, we propose a novel localization framework centered on a strapdown inertial navigation system, which receives high-precision pose corrections from image-based visible-light positioning. A structured LED array is deployed as a stable underwater positioning reference, and an adaptive optical signal processing method is developed to enhance the extraction of light spot features under challenging scenarios. To address the distortions introduced by cross-medium refraction, we introduce a compensation model that restores accurate camera pose estimation without the need for cumbersome recalibration. Extensive experimental evaluations demonstrate that the proposed system achieves millimeter-level accuracy under favorable scenarios and maintains centimeter-level robustness in unfavorable observation scenarios. Owing to its high-precision, robustness, and cost-effectiveness, the proposed approach holds significant promise for advancing autonomous underwater navigation and next-generation intelligent robotic systems.

Interferometric Synthetic Aperture Radar (InSAR)-derived Digital Elevation Models (DEMs) provide critical landform data for monitoring the stability of urban infrastructure, especially for linear infrastructure such as roads and transportation corridors. Traditional landform classification methods are often hindered by incomplete results and require significant manual intervention. To address these challenges, we propose an automated landform classification method based on an enhanced Random Forest (RF) model that integrates Optimization of Decreasing Reduction (ODR) for majority class undersampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVM-SMOTE) for minority class oversampling, specifically to address class imbalance. The method was validated using a dataset of 82,450 expert-labeled samples from approximately 100 km of highway corridors, with independent test sets and ten-fold cross-validation. The enhanced RF model achieved a classification completeness rate of 100% and a macro F-score of 97.0%, significantly outperforming traditional rule-based and standard RF methods. This approach provides robust post-processing support for InSAR-based urban infrastructure monitoring and environmental modeling.