The efficient utilization of underground spaces is a crucial strategy for mitigating land scarcity and expanding habitable environments. 3D Gaussian Splatting (3DGS) has emerged as a key enabler for enhancing robotic perception and spatial digitalization in underground spaces due to its unique advantages. However, illumination variation, sensor noise, and geometric degradation of underground spaces significantly degrade the localization accuracy, which compromises the robustness of existing Simultaneous Localization and Mapping (SLAM) systems. Therefore, we propose an RGB-D perception-enhanced 3DGS SLAM method. First, a multi-dimensional data enhancement and correction pipeline is introduced, integrating Multi-Scale Retinex with Color Restoration (MSRCR), Side Window Filtering (SWF), and adaptive gamma correction in the Hue-Intensity-Saturation (HIS) color space to improve low-light visual fidelity, while a depth completion network is developed to perform hole-filling in depth data. Second, a keyframe selection method based on the hybrid metric is proposed, which incorporates consistency constraints and multi-view overlap analysis to balance computational efficiency and representational completeness. Finally, a dual-constraint Gaussian management strategy is introduced, integrating an opacity threshold and observation frequency to filter out invalid Gaussian ellipsoids. At the same time, loop closure detection ensures global trajectory consistency and mapping accuracy. To validate the proposed method, experiments were conducted in typical underground spaces, including coal mine tunnels and underground parking garages, using a custom-designed underground mobile robot platform. The results demonstrate that, compared to state-of-the-art methods, the proposed method achieves a 13.8% improvement in Peak Signal-to-Noise Ratio (PSNR) over the best benchmark, while also achieving competitive trajectory accuracy and computational efficiency. These findings provide strong support for the development of digital twin systems in underground spaces.

Air pollution poses a significant threat to human health. Effective and efficient assessing individual exposure intensity will provide a crucial reference to mitigate potential air pollution risk. Previous proximity models in scenario simulation method neglect the variations among individual absorption efficiency. In this study, we proposed a novel demographic optimization proximity model (DOPM) to quantified sulfur dioxide (SO2) exposure risk under eight groups’ respiratory rates. In assessing exposure risk in Wuhan, one of the megacities in central China, we compared the capacity of DOPM and a classic proximity model on simulating exposure intensity, utilizing the near-Gaussian bi-square diffusion function and the filter optimal bandwidth. Subsequently, we utilized ordinary kriging (OK) interpolation to visualize the SO2 exposure risk map based on the simulation results of DOPM. We found that 9.5 km was the optimal bandwidth when near-Gaussian bi-square diffusion function utilized in proximity model. We also observed DOPM has stronger ability to simulate individual exposure intensity with a R-square of 0.44 when distinguished respiratory rate among receptors. These insights will aid public health researchers in assessing exposure risk across absorption efficiency and help local government officials devise more effective air pollution control strategies.

Digital elevation model (DEM) of difference (DoD) produced by unmanned aerial vehicle (UAV)-borne laser scanning (ULS) data has been one of the important methods for monitoring catchment-scale landscape change processes, while its accuracy has been limited by the lack of understanding for the spatially variable uncertainties from systematic errors and random errors included in the DoD. In this study, the DoD uncertainty derivation (DUD) method was improved by undertaking an exhaustive error analysis, estimation of residual systematic errors, and incorporating different DoD uncertainty elimination strategies, based on multitemporal ULS data acquired from a small catchment of the Chinese Loess Plateau. The adapted method was employed to estimate the soil erosion and deposition of the catchment, while the reliability of the method was verified by the volume of mass movement and the depths of gullies measured through field surveys. Results showed that mean systematic biases were 0.025, 0.008, -0.074, and 0.051 m for multitemporal point clouds, respectively. After coregistration, the corresponding systematic bias were 0.001, 0.008, -0.016, and -0.021 m, respectively. The change results showed a significant relationship with the results of mass movement and gully depths ($R^{2}~\gt 0.8$,$p~\lt 0.01$). The adapted DUD method was able to capture different erosion processes, including gully headcut retreat, gully development, mass movement, and localized deposition, while it also achieved an underestimation of the changes compared to field survey results. In the catchment, the area of human activity contributed the highest percentage of the volumetric changes, followed by the gully slope and gully bottom, and the hillslope normally contributed the lowest. Overall, the adapted DUD method provided a reliable way for estimating geomorphic changes at the catchment scale.

High-resolution topographic data acquired by remote aerial vehicles (RAVs) have facilitated the use of digital elevation model (DEM) and DEM of difference (DoD) methods for studying geomorphic processes in complex terrain. However, insufficient understanding of systematic bias and random errors for DEMs constrained the application. In this study, we comprehensively analyzed the spatial pattern and magnitude of errors (including systematic and random errors) of DEMs derived from RAV-acquired point clouds for a topographically complex area (a subcatchment of Qiaogou in the hilly and gully loess plateau (SC_QG), China). The relationships between random errors and influential factors associated with topography, point cloud density, vegetation, and interpolation algorithms were also evaluated. On this basis, an error source thresholding (EST) method was adapted through incorporating residual systematic errors and including more impacting factors in the fuzzy inference system for random error estimation. The adapted EST (AEST) method was then employed to quantify the DoD uncertainty and geomorphic changes in two small catchments with complex terrain (i.e., SC_QG and a sub-catchment of Telagou (SC_TLG) in the hilly and gully Loess Plateau, China), while the results were verified by the changes measured by terrestrial laser scanning (TLS) and erosion pins, respectively. Results showed that mean value of systematic errors of DEMs were 0.065 and 0.005 m for SC_QG and SC_TLG, while the residual errors were reduced to 0.002 and 0.001 m after co-registration, respectively. Significant statistical relationships (${p} \lt 0.01$) were found between random errors and influential factors. The erosional volume of two study sites detected by the adapted method were −252.29 and −981.07 m3 and the corresponding depositional volume were 30.57 and 1594.32 m3, respectively. The adapted method achieved a comparable pattern and magnitude of volumetric changes with TLS results, which was superior to the original EST method in SC_QG. Besides, our method showed a lower absolute error (0.034 m) compared to the original method (0.087 m) through a comparison with erosion pins measurement in the SC_TLG. Overall, the AEST method provided a reliable tool for geomorphic change detection in areas associated with complex terrain.

Apart from the applications of navigation, positioning, and timing, the Global Navigation Satellite System (GNSS) plays an important role in improving the quality and reliability of numerical weather prediction (NWP) models. However, the difference and contribution of assimilating GNSS-derived Zenith Total Delay (ZTD) and Precipitable Water Vapor (PWV) to forecast result are less investigated, which becomes the focus of this study. A unified method of assimilating GNSS-derived ZTD/PWV is first proposed, and their difference and contribution to the forecasting performance of Weather Research and Forecasting (WRF) model are quantitatively evaluated by focusing on the multiple meteorological parameters, such as precipitation, relative humidity, temperature, and pressure. In addition, the effects of magnitude and seasonal characteristics of GNSS-derived ZTD/PWV on the WRF model are further analyzed during a case of severe convective weather. Central and eastern China is selected as the study area, and 287 meteorological stations, 452 GNSS/Met stations, and 11 radiosonde stations are selected over the whole year of 2018. Results indicate that the assimilation of GNSS-derived ZTD/PWV, particularly ZTD, enhances the forecast accuracy of different meteorological parameters, and the positive contribution degree increases as the magnitude of GNSS-derived ZTD/PWV increases. Compared with the traditional method, the root mean square error reductions of precipitation, relative humidity, temperature, and pressure generated by a unified method of assimilating GNSS-derived ZTD/PWV are 31.9%/22.7%, 54.6%/44.0%, 44.7%/35.7%, and 37.1%/24.2%, respectively. These results show the feasibility and effectiveness of the proposed data assimilation method and verify the positive contribution of GNSS-derived tropospheric products in improving the performance of WRF model, especially for severe convective event nowcasting.

Road extraction from high-resolution remote sensing imagery is important and efficacious due to deep learning. However, most methods face challenges in capturing narrow roads, i.e. rural roads. In this paper, we propose a saliency-aware deep network (SAN) for narrow road extraction. Specifically, a multi-scale context module is employed to extract contextual features in different scales, and a global context module is followed to aggregate the above multi-scale context features, to improve the connectivity of narrow roads. In addition, motivated by visual saliency, a saliency-aware module is proposed to highlight roads during skip connections, to further separate narrow roads from complex backgrounds. In the experiments, SAN is compared with some state-of-the-art methods by using the DeepGlobe road dataset and achieves better performances, especially for narrow roads, which proves its superiority.

Aiming at the urgent demands on (near) real-time ionosphere products, we study the near real-time (NRT) modeling of the global ionospheric total electron content (TEC) by IGS hourly data and introduce the Kalman filter (KF) to solve the model parameters. The main objective of this article is to propose an adaptive method for determining the KF process noise covariance matrix. This method can reflect the change regularity of spherical harmonic (SH) between epochs and consider the impact of the current ionosphere level on SH. It can adaptively adjust the KF process noise covariance matrix of each epoch to improve the accuracy of NRT global ionosphere maps (GIMs). We analyze the effects of different initial values of the state vector and its covariance matrix on the SH coefficients and propose a method to avoid repeated filter initialization. The results show that for different initial values, the filter can reach the state of convergence within 6 h, but a high-precision initial value can significantly accelerate the KF convergence speed. Compared with Global Navigation Satellite System (GNSS) differential slant TEC (dSTEC) observables, the rms of our NRT products xrtg during quiet and magnetic storms are 1.47 and 1.56 TECU, respectively, larger than the postprocessed GIMs, but significantly smaller than those of real-time GIMs. Compared with Jason VTEC, the results also show that the accuracy of xrtg is even better than European Space Agency (ESA) final products during the magnetic storm.

Precipitable water vapor (PWV) is one of the key parameters in the evolution of extreme weather and climate change. However, current data fusion methods (such as Gaussian processes, spherical cap harmonics, and polynomial fitting) can hardly obtain simultaneously the PWV map with high precision and high spatiotemporal resolution. To solve this problem, a two-step-based PWV fusion (TPF) method is proposed, in which a hybrid PWV fusion model (HPFM) and a spatial and temporal fusion model (STFM) are introduced separately. In the first step, HPFM is established by combining the global pressure and temperature 2 wet (GPT2w) model, spherical harmonic functions, and polynomial fitting to obtain the PWV value with high precision at an arbitrary location in the study area. In the second step, STFM is proposed to generate the PWV map with high temporal resolution taking advantage of site-based global navigation satellite system (GNSS)-derived PWV. To validate the performance of the proposed method, GNSS observations, ERA-Interim, and ERA5 reanalysis products are selected in Yunnan Province, China, to carry out the experiment. Statistical results show that: 1) HPFM has the ability to obtain atmospheric water vapor with a root mean square (rms) of less than 3 mm in an arbitrary location of the PWV map and 2) STFM can generate PWV maps with the same temporal resolution as GNSS observations, and the accuracy of the obtained PWV values can be guaranteed. Therefore, the proposed TPF method is proven to have the ability to simultaneously retrieve PWV maps with high accuracy and spatiotemporal resolution.

Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.

Aerosol optical depth (AOD) is one of the basic parameters for determining the total aerosol content, and it exerts an important impact on regional environment pollution. To investigate the spatiotemporal variations of AOD, this study analyzes the relationship of AOD with precipitable water vapor (PWV) derived from a global navigation satellite system (GNSS) and meteorological parameters and proposes an adaptive AOD forecasting (AAF) model. In this model, the initial AOD value is determined using an empirical AOD model that considers annual periodicity, and the AOD difference is fitted using PWV, temperature ($T$), and surface pressure ($P$). In addition, this model also considers the time autocorrelation of the AOD difference; the model coefficients can be adaptively updated with training data. AOD data at 550 nm derived from the aerosol robotic network (AERONET), second modern-era retrospective analysis for research and applications (MERRA-2), and Copernicus atmosphere monitoring service (CAMS) for the Beijing–Tianjin–Hebei area are utilized to validate the proposed AAF model. Numerical results show that: 1) the accuracy of AOD derived from MERRA-2 is superior to that obtained from CAMS; 2) AOD is negatively correlated with$P$, is positively correlated with PWV and$T$, and has a high time autocorrelation with the AOD difference at consecutive times; and 3) the proposed AAF model demonstrates better performance than the traditional multiple linear regression (MLR) model. The average root mean square error (RMSE), mean absolute error (MAE), and bias of the AAF model are 0.17, 0.14, and −0.04, respectively, and those of the MLR model are 0.31, 0.25, and 0.06, respectively. These results reveal that the proposed AAF model can estimate AOD with high precision and has considerable potential for application in AAF research.

Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors' threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest.