The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.

Adverse weather conditions are one of the long-tailed problems facing autonomous driving. Solving the problem of autonomous driving operation in adverse weather conditions is an important challenge for realizing advanced autonomous driving. In order to enhance the LiDAR perception capability in snowy weather for autonomous driving, this study proposes a denoising method for multi-frame continuous point clouds. The core concept of this method is to allow ordered objects (e.g., stationary objects on the ground) to strengthen each other, while allowing disordered objects (e.g., snow) to weaken each other. This is done by first selecting three consecutive frames of the point cloud as a denoising unit, and then removing the ground points from each frame of the point cloud. After that, the point clouds from the first two frames are used as the source point clouds, and the point cloud from the third frame is used as the target point cloud for point cloud registration. Finally, the Time Outlier Removal (TOR) filter proposed in this paper combined with Entropy Weight Method (EWM) is utilized for denoising. The experimental results show that the performance of the method proposed in this paper exceeds the existing methods. In addition, the method in this paper not only removes the disordered snowflakes in the air, but also removes some other disordered noise points (e.g., the ghosting of the stationary objects), which provides an advantageous guarantee for the realization of the automatic driving in snowy weather.

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

Ture Digital orthophoto maps (TDOMs) have become essential sources of spatial geographic data, characterized by their high precision, detailed representation, and accessibility. However, the generation of orthophotos using image differential correction techniques, which utilize digital elevation models (DEM) and digital surface models (DSM), often results in significant non-orthogonal distortions. These distortions arise from insufficient building information and the inherent limitations of DSM accuracy. Conventional techniques for geometric stitching and uniform radiometric correction are limited in their ability to enhance orthophoto quality. Although advancements in radiance-field-based view synthesis techniques are notable, they face challenges related to training speed, rendering quality, and scene editability. Even cutting-edge technologies, such as Instant-NGP, struggle to balance orthophoto quality with computational efficiency. In response to these challenges, this paper introduces Ortho-3DGS, an innovative methodology for generating DOMs from UAV imagery via 3D Gaussian Splatting. Unlike methods based on Neural Radiance Fields (NeRF), our approach employs three-dimensional Gaussian ellipsoids to represent scenes, continuously refining Gaussian parameters through depth supervision and gradient optimization for explicit scene reconstruction. The proposed methodology includes an orthophoto rendering pipeline specifically designed to produce high-quality, distortion-free DOMs. Experimental findings demonstrate that Ortho-3DGS outperforms traditional algorithms, such as ContextCapture, Pix4Dmapper, and Metashape, as well as NeRF-based methods like Ortho-NeRF and Ortho-NGP, in both radiometric quality and geometric accuracy. By utilizing explicit scene representation, depth-supervised optimization, and an efficient orthophoto rendering pipeline, Ortho-3DGS effectively reconstructs geometric and color information, enabling rapid orthophoto generation. Furthermore, our method achieves geometric accuracy comparable to leading commercial photogrammetry software and state-of-the-art NeRF-based orthophoto algorithms, while effectively addressing distortions, biases, and artifacts in complex scenarios. These advancements establish Ortho-3DGS as a robust tool for high-precision orthophoto generation and a wide range of geospatial applications.

As autonomous driving technology advances, ensuring the system's safety in rain and snow has emerged as a pivotal research topic. In rainy and snowy weather, rain and snow can generate noise points within the point cloud captured by the Light Detection and Ranging (LiDAR), significantly impeding the LiDAR's sensing capability. To address this problem, we first manually label the point cloud data gathered in rain and snow, categorizing all points into noise points and non-noise points. Subsequently, we analyze the intensity and spatial distribution characteristics of the rain and snow noise points and employ the gamma distribution curve to illustrate the spatial distribution characteristics of these noise points. Finally, we propose a Low-Intensity Dynamic Statistical Outlier Removal (LIDSOR) filter, an enhancement of the existing Dynamic Statistical Outlier Removal (DSOR) filter. Experimental results suggest that the LIDSOR filter can effectively eliminate rain and snow noise points while preserving more environmental feature points. Additionally, it consumes fewer computational resources. The filter we propose in this paper significantly contributes to the safe operation of the autonomous driving system in diverse complex environments.

In the field of autonomous driving, a pressing issue is how to enable LiDAR to accurately perceive the 3-D environment around the vehicle without being affected by rain, snow, and fog. Specifically, rain, snow, and fog can be present within the LiDAR’s detection range and create noise points. To address this problem, we propose a unified denoising network, AdverseNet, for adverse weather point clouds, which is capable of removing noise points caused by rain, snow, and fog from LiDAR point clouds. In AdverseNet, we adopt the cylindrical triperspective view (CTPV) representation for point clouds and employ a two-stage training strategy. In the first training stage, generic features of rain, snow, and fog noise points are learned. In the second training stage, specific weather features are learned. We conducted comparative experiments on the DENSE dataset and the SnowyKITTI dataset, and the results show that the performance of our method on both datasets is significantly improved compared to other methods, with the Mean Intersection over Union (MIoU) reaching 94.67% and 99.33%, respectively. Our proposed AdverseNet enhances the LiDAR sensing capability in rain, snow, and fog, ensuring the safe operation of autonomous vehicles in adverse weather conditions. The source code is available at https://github.com/Naclzno/AdverseNet.