Airborne Laser Scanning (ALS) point clouds, characterized by low point density due to extensive ground coverage, pose significant challenges in labeling work. Additionally, the ALS scanning pattern can lead to occlusions, resulting in data gaps and further increasing the difficulty of accurately labeling points within obstructed areas. In this context, fully supervised deep learning methods, which rely on a large volume of precisely annotated point-level data, are not well-suited due to the time-consuming and costly nature of obtaining extensive labeled datasets. Addressing these challenges, we propose an innovative weakly supervised learning strategy: a dense supervised dual-aware contrastive learning approach (DSDCL). We introduce a context-aware contrastive constraint to enhance the model’s ability to differentiate similar data points by leveraging subtle contextual differences, improving feature learning robustness in environments with incomplete objects. Additionally, we apply a scene-aware dense contrastive constraint to enable multi-scale point cloud representations while enforcing class-aware feature space constraints through gradient-based learning across multiple layers. We employ this contrastive constraint to enhance category discrepancies in prototype features predicted by pseudo-labels from dual-branch data, improving model robustness to categorical distribution differences by focusing on class prototype features and mitigating the impact of erroneous pseudo-labels. Extensive experiments on three benchmark ALS datasets demonstrate that DSDCL achieves performance comparable to advanced fully supervised methods, even when using only 0.1% of labeled samples.

Accurate modeling of satellite clock bias (SCB) is critical for enhancing high-precision positioning capabilities. Existing approaches, such as semiparametric adjustment models and neural networks, address the nonlinearity and non-stationarity of SCB time series, as well as potential distortions from trend and noise component overlap. However, these methods encounter practical limitations, particularly in the selection of kernel functions for semiparametric models and the initialization of parameters for neural networks. To overcome these challenges, this paper introduces a novel integrated model called the Semi-LFA-Informer (SLFAI) model. Moreover, this model combines semiparametric techniques with optimized self-attention neural networks and is applied to predict SCB for BDS-3. Its performance is compared with other models, including quadratic polynomial (QP), spectral analysis (SA), and long short-term memory (LSTM) networks. The comparison is focused on prediction stability and accuracy. The experimental results show that the proposed method can not only effectively solve the problem of the generalization ability, but also significantly enhance the computational efficiency and accuracy. The SLFAI model achieves average prediction accuracies exceeding 0.15 ns, 0.25 ns, and 0.35 ns for 3-hour, 6-hour, and 12-hour forecasts, respectively, Meanwhile, compared with the other three models, The SLFAI model shows an average prediction accuracy improvement of approximately 53.6%, 59.4%, and 43.5% for the 3-hour, 6-hour, and 12-hour forecasts, respectively, representing a new approach to acquiring high-quality SCB.

The three-dimensional computerized ionospheric tomography (CIT) based on Global Navigation Satellite System (GNSS) data is a classic ill-posed inverse problem. This study proposes an algorithm based on the semi-parametric model, which leverages the non-parametric component in the semi-parametric model to address systematic errors in CIT, thus improving the accuracy and effectiveness of reconstructed ionospheric electron density (IED). The feasibility and effectiveness of the proposed algorithm in processing systematic errors and reconstructing IED values are validated through simulation and real data experiments. In the simulation experiment, the proposed algorithm can separate systematic errors effectively. Compared with the Tikhonov regularization algorithm, the proposed algorithm offers improvements of 50.2% and 50.3% in root mean square error ( RMSE ) and the mean absolute error ( Δ E ) for the reconstructed IEDs. The reconstructed three-dimensional ionospheric structure based on real data is consistent with the real spatiotemporal variation characteristics of the ionosphere. The average RMSE and Δ E of the reconstructed slant total electron content (STEC) using the proposed algorithm are 30.2% and 32.1% higher than those of the Tikhonov regularization algorithm, respectively. The proposed algorithm demonstrates superior reconstruction performance in several aspects.