Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

In this study, we focus on the kinematic precise point positioning (PPP) solutions at high‐latitudes during the March 2015 great geomagnetic storm. We aim to discover the mechanism behind the positioning degradation from the perspective of the impacts of the storm‐induced ionospheric disturbance on the global navigation satellite system (GNSS) data processing. We observed that the phase scintillation dominated the amplitude scintillation at high‐latitudes and the variation pattern of the rate of total electron content index (ROTI) was consistent with that of the phase scintillation during the storm. The kinematic PPP errors at high‐latitudes were almost three times larger than those at the middle‐ and low‐latitude, which were accompanied by large ROTI variations. From the perspective of GNSS data processing, the large positioning errors were also found to be related to the large number of satellites experiencing cycle slips (CSs). Based on the lock time from the ionospheric scintillation monitoring receiver, we found that a large amount of the CSs was falsely detected under the conventional threshold of the CS detector. By increasing such threshold, the kinematic positioning accuracy at high‐latitudes can be improved to obtain similar magnitude as at middle‐ and low‐latitude. The improved positioning accuracy may suggest that the ionospheric disturbance induced by the geomagnetic storm at high‐latitudes has minor effects on triggering the CSs. Therefore, precise positioning can be achieved at high‐latitudes under geomagnetic storms, given that the CS problem is well addressed.

An improved ionospheric tomography algorithm is developed for the tomographic reconstruction of the ionospheric electron density distribution based on the automatic search technology of relaxation factor, in which the automatic search technology is a training process to optimize the relaxation factors of the iterative algorithm. In comparison with some classical tomography algorithms, the proposed algorithm can not only greatly improve the efficiency of inversion but also obtain ionospheric electron density images with high fidelity. A careful validation of the proposed algorithm is performed by conducting numerical experiments with Global Positioning System simulation and real data, and according to the results of the quantitative comparison and statistical analysis, compared with other classical ionospheric tomography algorithms, the proposed algorithm exhibits significantly reconstruction accuracy.

Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather.

Two solar flares accompanied by coronal mass ejections (CMEs) occurred on 24–25 February (DOY 055–056), 2023, resulting in a large magnetic storm on DOY 058. We reconstructed the ionospheric electron density (IED) in Europe to analyze the spatial distribution of ionosphere and its temporal evolution during this period. Computerized ionospheric tomography based on machine learning (CIT‐ML) was used to predict the IEDs of unobserved voxels. The IEDs were examined using observation data from the Swarm satellite. The CIT‐ML accuracy was 28.3% higher than the improved algebraic reconstruction technique with relaxation factor automatic search technology (IART‐AS), which effectively improved the typical ill‐posed problem of CIT. The first flare generated the Bz component of the interplanetary magnetic field (IMF), which continued southward for 13 hr, causing a small magnetic storm before the second flare occurred, resulting in an increased nighttime IED and nighttime medium‐scale traveling ionospheric disturbance (MSTID). The vertical total electron content (VTEC) and IED declined in the early stages of the main phase of the large magnetic storm, but later increased, indicating that negative‐positive biphasic storms were occurring in the ionosphere that altered the ionospheric daily cycle, resulting in the peak of the ionosphere being advanced by approximately 1.5 hr. The storms also caused nighttime MSTIDs during the main phase (DOY 057 at night) and the recovery phase (DOY 058 at night). To investigate the mechanisms of these results, we conducted a term analysis of the ion continuity equation using the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). The analysis showed that ambipolar diffusion driver nighttime MSTIDs during flares, while increased geomagnetic disturbances amplify the effects of neutral wind transport, E × B drift and chemical reactions during magnetic storms. These combined effects offset the alternating positive and negative structures induced by ambipolar diffusion, becoming the main cause of electron density variations during ionospheric storms.