Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method.

Based on the daily Japanese 55-year Reanalysis (JRA-55) dataset, this study reveals that southern Europe/western Asia (SEWA) and northern China are two large-scale regions that have exhibited consistent interdecadal variations in the frequency of persistent hot events (PHEs). Over the past 58 summers, the period approximately from 1973 to 1996 represents an inactive period for the occurrence of PHEs over the two regions, whereas the antecedent and subsequent periods are active periods. At the subseasonal time scale, the regional PHEs over SEWA are characterized by quasi-stationary wave train anomalies aloft from the northwest Atlantic to Europe, while the regional PHEs over northern China are characterized by quasi-stationary wave train anomalies over the Eurasian continent. The persistence of the quasi-stationary anomalies is associated with the Rossby wave propagation. Moreover, the energy extraction from the basic flow is also favorable for their persistence. Our study reveals that the above typical circulation anomalies for the PHEs over both SEWA and northern China are in phase with the background circulation changes during the two active periods. Thus, the interdecadal changes in background circulation can modulate the frequency of PHEs over the two regions simultaneously. Further analysis reveals that the background circulation changes are closely related to the interdecadal variation in the Silk Road pattern based on their similarities in both spatial pattern and temporal variation. The sea surface temperature over four particular regions seems to facilitate the phase shifts in the Silk Road pattern on the interdecadal time scale.

With the rapid development of Micro-Electro-Mechanical Systems (MEMS), smartphones become one of the most convenient tools to realize pedestrian positioning during daily life. Meanwhile, indoor positioning is a crucial part of pedestrian positioning. Facing the challenges associated with insufficient Global Navigation Satellite System (GNSS) coverage, landmark deployment costs, and complex and changeable obstacles in pedestrian indoor positioning, we propose a Factor Graph Optimization (FGO) based adaptive pedestrian dead reckoning (APDR)/up-view image (UPV)/Light integrated indoor positioning method. Firstly, to improve the positioning accuracy of the traditional pedestrian dead reckoning (PDR), we propose an APDR method. This method builds a height-based stride length estimation model and a unified heading error calibration model, improving the accuracy of stride length estimation and heading estimation. Secondly, we propose a light-aid up-view-visual-based indoor positioning method. The scenes in the up-view perspective are simpler and less changeable compared to side-view scenarios, reducing the image processing difficulty and labeling cost. Since lights are widely present in indoor areas, this algorithm utilizes lights as landmarks, eliminating the need for additional anchor deployment. During positioning, the handhold mode aligns more with the natural state of pedestrians using navigation apps on their phones. The up-view images are easily captured in this mode. We use edge operators to realize the light detection and extraction, thereby realizing the single-point position estimation. Then, we utilize a light-intensity-based light sequence matching module to extend the single-point position estimation to the kinematic positioning. Finally, we use the APDR factor, the UPV factor, and the light factor to construct the FGO function, realizing an APDR/UPV/Light multi-sensor-based indoor positioning method. To verify the performance of the proposed methods, we conducted pedestrian positioning experiments with a Huawei Mate40 Pro. The results indicated the feasibility of the APDR method and the light-aid up-view-visual-based indoor positioning method. Additionally, the superior positioning performance of the FGO-based APDR/UPV/Light integrated indoor positioning method is validated.

Photon counting LiDAR has revolutionized the field of LiDAR technology with its exceptional single-photon sensitivity and picosecond-level time resolution. It is particularly effective for detecting ultralong-range targets and measuring global ecosystems. Timing histograms and waveforms play vital roles in these applications, as they contain rich structural information about the targets. To systematically explore the performance boundary model for full-waveform applications in photon counting LiDAR, we have developed a model based on underlying theory and feasibility, which overcomes the limitation of detecting fewer than 5% of illumination cycles. By using the cumulative emission pulse number as the objective function, we establish the performance boundary model for full-waveform in photon counting LiDAR, revealing the relationship between the accuracy of the full-waveform and system parameters. The model’s accuracy is verified through theoretical analysis and experimental validation. Subsequently, we utilize Pareto optimality to determine the optimal parameters for the full-waveform performance boundary model. Experimental data indicates that to ensure a normalized root mean squared error (nRMSE) of less than 0.03 between full-waveform and ideal waveform, the performance boundaries are as follows: the optimal time bin width is 256 ps, the signal intensity falls within [0.8, 1.6], the tolerable noise is [0, 0.63 M] Hz, and the minimum cumulative pulses required is between [282, 319], given that the echo width is 5 ns. Finally, we discuss the practical application of the full-waveform performance boundary in photon counting LiDAR for complex target detection scenarios. Under the optimal parameter configuration, the R-square ( ${R^{2}}$ ) between the full-waveform and the ideal waveform consistently exceeds 90%. This work not only expands the range of applications for photon counting LiDAR in the field of full-waveform, but also establishes a strong connection with full-waveform processing algorithms.