Search Results (847)

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

Hundreds of Japanese features of war (field positions, tunnels, and fortifications) were constructed in Hong Kong during World War II. However, most of them were poorly documented and were left unknown but still in relatively good condition because of their durable design, workmanship, and remoteness. These features of war form parts of Hong Kong’s brutal history. Conservation, at least in digital form, is worth considering. With the authors coming from multidisciplinary and varied backgrounds, this paper aims to explore these features using a scientific workflow. First, we reviewed the surviving archival sources of the Imperial Japanese Army and Navy. Second, airborne LiDAR data were used to form territory digital terrain models (DTM) based on the Red Relief Image Map (RRIM) for identifying suspected locations. Third, field expeditions of searching for features of war were conducted through guidance of Global Navigation Satellite System—Real-Time Kinetics (GNSS-RTK). Fourth, the found features were 3D-laser scanned to generate mesh models as a digital archive and validate the findings of DTM-RRIM. This study represents a reverse-engineering effort to reconstruct the planned Japanese defense tactics of guerilla fight and Kamikaze grottos that were never used in Hong Kong. Full article

The demand for large-scale, heavy-duty autonomous pavement-sweeping robots is rising due to urban growth, hygiene needs, and labor shortages. Ensuring their health and safe operation in dynamic outdoor environments is vital, as terrain unevenness and slope gradients can accelerate wear, increase maintenance costs, and pose safety risks. This study introduces an AI-driven condition monitoring (CM) framework designed to detect terrain unevenness and slope gradients in real time, distinguishing between safe and unsafe conditions. As system vibration levels and energy consumption vary with terrain unevenness and slope gradients, vibration and current data are collected for five CM classes identified: safe, moderately safe terrain, moderately safe slope, unsafe terrain, and unsafe slope. A simple-structured one-dimensional convolutional neural network (1D CNN) model is developed for fast and accurate prediction of the safe to unsafe classes for real-time application. An in-house developed large-scale autonomous pavement-sweeping robot, PANTHERA 2.0, is used for data collection and real-time experiments. The training dataset is generated by extracting representative vibration and heterogeneous slope data using three types of interoceptive sensors mounted in different zones of the robot. These sensors complement each other to enable accurate class prediction. The dataset includes angular velocity data from an IMU, vibration acceleration data from three vibration sensors, and current consumption data from three current sensors attached to the key motors. A CM-map framework is developed for real-time monitoring of the robot by fusing the predicted anomalous classes onto a 3D occupancy map of the workspace. The performance of the trained CM framework is evaluated through offline and real-time field trials using statistical measurement metrics, achieving an average class prediction accuracy of 92% and 90.8%, respectively. This demonstrates that the proposed CM framework enables maintenance teams to take timely and appropriate actions, including the adoption of suitable maintenance strategies. Full article

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

Orientation entropy serves as a critical metric for assessing the directional disorder of urban street networks. However, conventional two-dimensional (2D) approaches neglect vertical variations, limiting their applicability in cities with complex terrains. This study proposes a three-dimensional (3D) orientation entropy framework by integrating elevation data, providing a more comprehensive assessment of urban spatial complexity. We developed a computational workflow combining ArcGIS 10.8 for spatial data extraction and Python 3.10.10 for entropy calculation. A case study in Chongqing, China, explores the relationship between 3D orientation entropy and residents’ perceptions of spatial disorder through a small-scale survey. Although no statistically significant correlation was observed, the findings suggest emerging patterns and underscore the necessity of multidimensional frameworks in evaluating urban spatial experience. This research contributes a novel metric to urban design assessment, particularly in topographically diverse environments, and offers a foundation for future empirical studies. Full article

Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article

Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in the new generation of communication systems. They serve as access points, base stations, relays, and gateways to extend network coverage, enhance connectivity, or offer communications services in places lacking telecommunication infrastructure. However, optimizing UAV placement in three-dimensional (3D) environments with diverse user distributions and uneven terrain conditions is a crucial challenge. Therefore, this paper proposes a novel framework to minimize UAV transmission power while ensuring a guaranteed data rate in realistic and complex scenarios. To this end, using the particle swarm optimization evolution (PSO-E) algorithm, this paper analyzes the impact of user-truncated distribution models for suburban, urban and dense urban environments. Extensive simulations demonstrate that dense urban environments demand higher power than suburban and urban environments, with uniform user distributions requiring the most power in all scenarios. Conversely, Gaussian and exponential distributions exhibit lower power requirements, particularly in scenarios with concentrated user hotspots. The proposed model provides insight into achieving efficient network deployment and power optimization, offering practical solutions for future communication networks in complex 3D scenarios. Full article

Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper proposes a coverage path planning algorithm for Unmanned Aerial Vehicles (UAVs) that minimizes energy consumption while satisfying a set of other requirements, such as coverage and observation resolution. To deal with these issues, we propose a novel energy-optimal coverage path planning framework for monitoring tasks. Firstly, the 3D terrain’s spatial characteristics are digitized through a combination of parametric modeling and meshing techniques. To accurately estimate actual energy expenditure along a segmented trajectory, a power estimation module is introduced, which integrates dynamic feasibility constraints into the energy computation. Utilizing a Digital Surface Model (DSM), a global energy consumption map is generated by constructing a weighted directed graph over the terrain. Subsequently, an energy-optimal coverage path is derived by applying a Genetic Algorithm (GA) to traverse this map. Extensive simulation results validate the superiority of the proposed approach compared to existing methods. Full article

This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational intensity in accordance with terrain complexity. This approach effectively balances the global feature retention of point cloud data with computational efficiency, making it highly adaptable to the growing trend of large-scale 3D point cloud datasets. DFPS is designed with a multithreaded parallel acceleration architecture, which significantly enhances processing speed. Experimental results demonstrate that, for a point cloud dataset containing millions of points, DFPS reduces processing time from approximately 161,665 s using the original FPS method to approximately 71.64 s at a 12.5% sampling rate, achieving an efficiency improvement of over 2200 times. As the sampling rate decreases, the performance advantage becomes more pronounced: at a 3.125% sampling rate, the efficiency improves by nearly 10,000 times. By employing visual observation and quantitative analysis (with the chamfer distance as the measurement index), it is evident that DFPS can effectively preserve global feature information. Notably, DFPS does not depend on GPU-based heterogeneous computing, enabling seamless deployment in resource-constrained environments such as airborne and mobile devices, which makes DFPS an effective and lightweighting tool for providing high-quality input data for subsequent algorithms, including point cloud registration and semantic segmentation. Full article

The safe disposal of high-level radioactive waste (HLW) is imperative for sustaining China’s rapidly expanding nuclear power sector, with deep geological repositories requiring rigorous site evaluation via underground research laboratories (URLs). This study presents a controlled-source audio-frequency magnetotellurics (CSAMT) survey at the Xinchang site in China’s Beishan area, a region dominated by high-resistivity metamorphic rocks. To overcome electrical data acquisition challenges in such resistive terrains, salt-saturated water was applied to transmitting and receiving electrodes to enhance grounding efficiency. Using excitation frequencies of 9600 Hz to 1 Hz, the survey achieved a 1000 m investigation depth. Data processing incorporated static effect removal via low-pass filtering and smoothness-constrained 2D inversion. The results showed strong consistency between observed and modeled data, validating inversion reliability. Borehole correlations identified a 600-m-thick intact rock mass, confirming favorable geological conditions for URL construction. The study demonstrates CSAMT’s efficacy in characterizing HLW repository sites in high-resistivity environments, providing critical geophysical insights for China’s HLW disposal program. These findings advance site evaluation methodologies for deep geological repositories, though integrated multidisciplinary assessments remain essential for comprehensive site validation. This work underscores the feasibility of the Xinchang site while establishing a technical framework that is applicable to analogous challenging terrains globally. Full article

This entry gives an overview of the main data structures and approaches used for a two-dimensional representation of the terrain surface using a digital elevation model (DEM). A DEM represents the elevation of the earth surface from a set of points. It is used for terrain analysis, visualisation and interpretation. DEMs are most commonly defined as a grid where an elevation is assigned to each grid cell. Due to its simplicity, the square grid structure is the most common DEM structure. However, it is less adaptive and shows limitations for more complex processing and reasoning. Hence, the triangulated irregular network is a more adaptive structure and explicitly stores the relationships between the points. Other topological structures (contour graphs, contour trees) have been developed to study terrain morphology. Topological relationships are captured in another structure, the surface network (SN), composed of critical points (peaks, pits, saddles) and critical lines (thalweg, ridge lines). The SN can be computed using either a TIN or a grid. The Morse Theory provides a mathematical approach to studying the topology of surfaces, which is applied to the SN. It has been used for terrain simplification, multi-resolution modelling, terrain segmentation and landform identification. The extended surface network (ESN) extends the classical SN by integrating both the surface and the drainage networks. The ESN can itself be extended for the cognitive representation of the terrain based on saliences (typical points, lines and regions) and skeleton lines (linking critical points), while capturing the context of the appearance of landforms using topo-contexts. Full article

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework’s efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments. Full article

This article presents a methodology for developing a three-dimensional terrain model based on numerical data in the form of a point cloud, with an emphasis on reducing mesh surface errors and using a surface smoothing factor. Initial surface generation was based on a point cloud with a square mesh, and an adopted algorithm for mesh conversion to the input form for the computer aided design (CAD) environment was presented. The use of a bilinear interpolation algorithm was proposed to reduce defects in the three-dimensional surface created in the reverse engineering process. The terrain mapping accuracy analyses were performed for three samples of different geometry using two available options in the Siemens NX program. All obtained surfaces were subjected to shape deviation analysis. For each of the analyzed surfaces, changing the smoothing factor from 0% to 15% did not cause significant changes in accuracy depending on the method adopted. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 6.16%, and in the Variable Density (VD) method, it was within the range of 5.01–6%. For steep regions, in the UD method, it was 6.25%, and in the VD method, it was within the range of 5.39–6.47%, while for concave–convex regions, in the UD method, it was 6.5% and in the VD method, it was within the range of 4.96–6.36%. For a smoothing factor value of 20%, a sudden increase in the inaccuracy of the shape of the obtained surface was observed. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 69.84%, and in the Variable Density (VD) method, it was 71.62%. For steep regions, in the UD method, it was 76.07%, and in the VD method, it was 80.94%, while for concave–convex regions, in the UD method, it was 56.08%, and in the VD method, it was 62.38%. The developed methodology provided high accuracy in the reproduction of numerical data that can be used for further analyses and manufacturing processes, such as 3D printing. Based on the obtained data, three fused deposition model (FDM) prints were made, presenting each of the analyzed types of terrain geometry. Only FDM printing was used, and other technologies were not verified. Full article