Search Results (78)

In road scenes, the collected 3D point cloud data is usually accompanied by a large amount of interference mainly composed of ground point clouds and the property of uneven density distribution, which will bring difficulties to subsequent recognition and prediction. To address these problems, this paper proposes a ground point cloud segmentation algorithm based on local density plane fitting. Firstly, for the uneven density distribution of 3D point clouds, density segmentation is used to obtain several regions with balanced density. Then, candidate sample selection and plane validity detection are carried out for each region. The modified classical DBSCAN clustering algorithm is used to obtain effective fitting planes and perform clustering according to the fitting planes. Finally, different planes are divided according to the clustering results, and abnormal inspection is performed on the obtained results to screen out the most reasonable result. This scheme can effectively improve the scalability of the algorithm, reduce training costs, and improve deployment efficiency and universality. Experimental results show that the algorithm used in this paper has advantages compared with advanced algorithms of the same category, and can greatly reduce ground interference. Full article

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m² in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m², respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios. Full article

Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output. Full article

Deep gravitational slope deformations (DsGSDs) are a geological and engineering challenge with important implications for slope stability, the reliability of existing infrastructures, land use and, above all, the safety of settlements. This paper focuses on the DsGSD phenomenon that affects a large part of the Borrano hamlet, located in the municipality of Civitella del Tronto (Abruzzo Region, Central Italy). This instability is characterized by slow movements of large volumes of material. The main factors initiating deformations are a combination of geological and hydrogeological aspects. These factors include the complex local stratigraphy, composed of pelitic and arenaceous facies at high slope dip angles, and extreme natural events such as heavy rainfall and earthquakes. This study employs a multidisciplinary approach integrating in field activities such as remote-controlled surface monitoring (clinometers and strain gauges), in-depth monitoring (inclinometers and piezometers), aero-photogrammetric analysis and numerical modelling. These techniques permitted us to characterize the evolution of the slope and to identify both the critical sliding surfaces and the mechanisms governing the ground movements. Soil deformations were mainly observed in the central zone of the hamlet. Significant deformations were recorded along planes of weakness at depth between arenaceous and pelitic materials. These planes represent contact zones between the clayey–marly facies, characterized by low strength, and the arenaceous facies, characterized by higher stiffness, creating a mechanical contrast that favours the development of large deformations. The numerical analyses confirmed good correlation with the monitoring data, revealing in detail the instability of both local and territorial processes. The 3D numerical analysis showed how the movements are controlled by planes of weakness, highlighting the key rule of geological discontinuities. Full article

Ground-penetrating radar (GPR) is an effective geophysical method for rapid and non-destructive detection. Directional borehole radar is the application of GPR in a borehole, which can determine the depth, orientation, and distance of the target from the borehole. The borehole radar azimuth recognition algorithm is based on the assumption of far-field plane waves. Therefore, in the near-field area where the target is closer to the borehole, the electromagnetic waves reflected by the target cannot be regarded as plane waves but will have a certain curvature. The plane wave assumption is not valid in this area, so the azimuth recognition algorithm will have significant errors, forming blind zones for directional borehole radar detection. This article uses the finite-difference time-domain (FDTD) algorithm to numerically simulate how blind zones affect directional borehole radar systems, identify the impact patterns, and minimize them. After calculation and numerical simulation verification, it has been found that when the center frequency of the antenna is 1 GHz, within 2 m of the target from the borehole, there is a significant error in azimuth recognition, which can be defined as the near-field region. Similarly, through numerical simulation verification, the optimal antenna center frequency is between 600 MHz and 1100 MHz. Oil-based mud is superior to water-based mud. The optimal antenna center frequency decreases as the target distance increases. Full article

With the aging of the population and the increase in the number of people with disabilities, intelligent wheelchairs are essential in improving travel autonomy and quality of life. In this paper, we propose an autonomous outdoor navigation framework for intelligent wheelchairs based on hierarchical cost maps to address the challenges of wheelchair navigation in complex and dynamic outdoor environments. First, the framework integrates multi-sensors such as RTK high-precision GPS, IMU, and 3D LIDAR; fuses RTK, IMU, and odometer data to realize high-precision positioning; and performs path planning and obstacle avoidance through dynamic hierarchical cost maps. Secondly, the drivable area layer is integrated into the traditional hierarchical cost map, in which the drivable area detection algorithm utilizes local plane fitting and elevation difference analysis to achieve efficient ground point cloud segmentation and real-time updating, which ensures the real-time safety of navigation. The experiments are validated in real outdoor scenes and simulation environments, and the results show that the speed of drivable region detection is about 30 ms, the positioning accuracy of wheelchair outdoor navigation is less than 10 cm, and the distance of active obstacle avoidance is 1 m. This study provides an effective solution for the autonomous navigation of the intelligent wheelchair in a complex outdoor environment, and it has a high robustness and application potential. Full article

Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed. Full article

Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides. Full article

The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs. Full article

3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. Therefore, a small-object-detection algorithm based on clustering is proposed. Firstly, a new segmented ground-point clouds segmentation algorithm is proposed, which filters out the object point clouds according to the heuristic rules and realizes the ground segmentation by multi-region plane-fitting. Then, the small-object point cloud is clustered using an improved DBSCAN clustering algorithm. The K-means++ algorithm for pre-clustering is used, the neighborhood radius is adaptively adjusted according to the distance, and the core point search method of the original algorithm is improved. Finally, the detection of small objects is completed using the directional wraparound box model. After extensive experiments, it was shown that the precision and recall of our proposed ground-segmentation algorithm reached 91.86% and 92.70%, respectively, and the improved DBSCAN clustering algorithm improved the recall of pedestrians and cyclists by 15.89% and 9.50%, respectively. In addition, visualization experiments confirmed that our proposed small-object-detection algorithm based on the point-cloud clustering method can realize the accurate detection of small objects. Full article

The rapid urbanization and growing number of factories, human population, and motor vehicles have led to a drastic increase in the concentration of air pollutants. This smog is one of the most important disturbances in city planning. Urban trees play a vital role in the improvement of air quality. The selection of high-potential trees to capture air pollutants provides an attractive route for the mitigation of urban smog. The current study explored the air purification potential of the four most abundant trees, i.e., white mulberry (Morus alba L.), plane tree (Platanus orientalis L.), European ash (Fraxinus excelsior L.), and Tehran pine (Pinus eldarica Ten.)], as phytoremediators grown in three parks located in regions with low, moderate, and high levels of air pollution in Tehran on the mitigation of four urban hazardous gases (O₃, NO₂, CO, and SO₂) and in altering the content of respiratory gases (CO₂ and O₂). The measurement of gas levels was carried out in September–October, from 1.30 to 1.50 m above the ground. The concentration of gases was measured by an ambient gas assessment device (Aeroqual). Broad-leaf deciduous species had a greater ability to mitigate O₃, NO₂, CO, CO₂, and SO₂ concentrations than needle-leaf evergreen species. The lowest levels of O₃ and CO were found around P. orientalis (0.035 and 0.044 ppm, respectively), whereas the content of O₂ was the highest in the atmosphere of this tree (20.80 ppm). The lowest content of NO₂ (0.081 ppm) and SO₂ (0.076 ppm) was determined in the vicinity of M. alba and F. excelsior, respectively. Among the studied species, P. orientalis proved to be the best for air phytoremediation, effectively mitigating hazardous gases more than the other species. Conversely, P. eldarica is not recommended for air phytoremediation in urban green spaces. Future research should focus on exploring a wider range of tree species and their potential for air pollution mitigation in diverse urban settings across different seasons and climatic conditions. Full article

This study introduces a novel satellite image digital surface model (DSM) reconstruction framework grounded in deep learning methodology. The proposed framework effectively utilizes a rational polynomial camera (RPC) model to establish the mapping relationship between image coordinates and geographic coordinates. Given the expansive coverage and abundant ground object data inherent in satellite images, we designed a lightweight deep network model. This model facilitates both coarse and fine estimation of a height map through two distinct stages. Our approach harnesses shallow and deep image information via a feature extraction module, subsequently employing RPC Warping to construct feature volumes for various angles. We employ variance as a similarity metric to achieve image matching and derive the fused cost volume. Following this, we aggregate cost information across different scales and height directions using a regularization module. This process yields the confidence level of the current height plane, which is then regressed to predict the height map. Once the height map from stage 1 is obtained, we gauge the prediction’s uncertainty based on the variance in the probability distribution in the height direction. This allows us to adjust the height estimation range according to this uncertainty, thereby enabling precise height value prediction in stage 2. After conducting geometric consistency detection filtering of fine height maps from diverse viewpoints, we generate 3D point clouds through the inverse projection of RPC models. Finally, we resample these 3D point clouds to produce high-precision DSM products. By analyzing the results of our method’s height map predictions and comparing them with existing deep learning-based reconstruction methods, we assess the DSM reconstruction performance of our proposed framework. The experimental findings underscore the robustness of our method against discontinuous regions, occlusions, uneven illumination areas in satellite imagery, and weak texture regions during height map generation. Furthermore, the reconstructed digital surface model (DSM) surpasses existing solutions in terms of completeness and root mean square error metrics while concurrently reducing the model parameters by 42.93%. This optimization markedly diminishes memory usage, thereby conserving both software and hardware resources as well as system overhead. Such savings pave the way for a more efficient system design and development process. Full article

The ground-motion simulation of regional-specific earthquake scenarios is crucial for the seismic design of key facilities. Herein, we considered parameter uncertainty in ground-motion simulations and the impact of multiple faults when determining the maximum credible ground-motion parameters of large earthquakes at a near-field dam. The source models of the Daju–Lijiang, Xiaozhongdian–Daju, and Longpan–Qiaohou faults were established based on geological and geophysical data. Although the method for identifying asperity is not yet mature and still faces many difficulties, it provides an opportunity to identify the non-uniform slip distribution on the rupture plane by earthquake scenarios. A multi-scheme stochastic finite-fault simulation method was then used to estimate the minimum; mean; maximum; and 50th-, 84th-, and 95th-percentile values of the peak ground acceleration and pseudo-spectral acceleration response spectra. The results showed that the Longpan–Qiaohou fault can generate the largest ground-motion parameters compared with the other two faults. Moreover, this result was supported by the statistical analysis of the results of twelve thousand simulations of these three faults. Thus, it can be concluded that the maximum credible ground-motion parameters are represented by the 84th-percentile pseudo-spectral acceleration response spectrum of the Longpan–Qiaohou fault. This finding will benefit the seismic safety design of the target dam. More importantly, this multi-scheme method can be applied to other key facilities to obtain reasonable ground-motion parameters. Full article

Fractured rock mass is extensively distributed in Karst topography regions, and its geological environment is different from that of the quaternary strata. In this study, the influences on geological environment induced by the construction and operation of a large-scale borehole group of ground source heat pumps are analyzed by a thermo-hydro-mechanical (THM) coupling numerical model. It was found that groundwater is redirected as the boreholes can function as channels to the surface, and the flow velocity in the upstream of borehole group is higher than those downstream. This change in groundwater flow enhances heat transfer in the upstream boreholes but may disturb the original groundwater system and impact the local geological environment. Heat accumulation is more likely to occur downstream. The geo-stress concentration appears in the borehole area, mainly due to exaction and increasing with the depth. On the fracture plane, tensile stress and maximum shear stress simultaneously occur on the upstream of boreholes, inducing the possibility of fracturing or the expansion of existing fractures. There is a slight uplift displacement on the surface after the construction of boreholes. The correlations of the above THM phenomena are discussed and analyzed. From the modeling results, it is suggested that the consolidation of backfills can minimize the environmental disturbances in terms of groundwater redirection, thermal accumulation, occurrence of tensile stress, and possible fracturing. This study provides support for the assessment of impacts on geological environments resulting from shallow geothermal development and layout optimization of ground heat exchangers in engineering practices. Full article

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed. Full article