Explore the vast range of titles available.

Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide more informative and visual forecasts about weather systems in realistic space, has been less explored. Thus, this paper proposes a 3D-convolutional long short-term memory (ConvLSTM)-based model to perform three-dimensional gridded radar echo extrapolation for severe storm nowcasting. First, a 3D-convolutional neural network (CNN) is used to extract the 3D spatial features of each input grid radar volume. Then, 3D-ConvLSTM layers are leveraged to model the spatial–temporal relationship between the extracted 3D features and recursively generate the 3D hidden states correlated to the future. Nowcasting results are obtained after applying another 3D-CNN to up-sample the generated 3D hidden states. Comparative experiments were conducted on a public National Center for Atmospheric Research Data Archive dataset with a 3D optical flow method and other deep-learning-based models. Quantitative evaluations demonstrate that the proposed 3D-ConvLSTM-based model achieves better overall and longer-term performance for storms with reflectivity values above 35 and 45 dBZ. In addition, case studies qualitatively demonstrate that the proposed model predicts more realistic storm evolution and can facilitate early warning regarding impending severe storms.

UAV LiDAR systems have unique advantage in acquiring 3D geo-information of the targets and the expenses are very reasonable; therefore, they are capable of security inspection of high-voltage power lines. There are already several methods for power line extraction from LiDAR point cloud data. However, the existing methods either introduce classification errors during point cloud filtering, or occasionally unable to detect multiple power lines in vertical arrangement. This paper proposes and implements an automatic power line extraction method based on 3D spatial features. Different from the existing power line extraction methods, the proposed method processes the LiDAR point cloud data vertically, therefore, the possible location of the power line in point cloud data can be predicted without filtering. Next, segmentation is conducted on candidates of power line using 3D region growing method. Then, linear point sets are extracted by linear discriminant method in this paper. Finally, power lines are extracted from the candidate linear point sets based on extension and direction features. The effectiveness and feasibility of the proposed method were verified by real data of UAV LiDAR point cloud data in Sichuan, China. The average correct extraction rate of power line points is 98.18%.

In recent years, deep learning-based models have produced encouraging results for hyperspectral image (HSI) classification. Specifically, Convolutional Long Short-Term Memory (ConvLSTM) has shown good performance for learning valuable features and modeling long-term dependencies in spectral data. However, it is less effective for learning spatial features, which is an integral part of hyperspectral images. Alternatively, convolutional neural networks (CNNs) can learn spatial features, but they possess limitations in handling long-term dependencies due to the local feature extraction in these networks. Considering these factors, this paper proposes an end-to-end Spectral-Spatial 3D ConvLSTM-CNN based Residual Network (SSCRN), which combines 3D ConvLSTM and 3D CNN for handling both spectral and spatial information, respectively. The contribution of the proposed network is twofold. Firstly, it addresses the long-term dependencies of spectral dimension using 3D ConvLSTM to capture the information related to various ground materials effectively. Secondly, it learns the discriminative spatial features using 3D CNN by employing the concept of the residual blocks to accelerate the training process and alleviate the overfitting. In addition, SSCRN uses batch normalization and dropout to regularize the network for smooth learning. The proposed framework is evaluated on three benchmark datasets widely used by the research community. The results confirm that SSCRN outperforms state-of-the-art methods with an overall accuracy of 99.17%, 99.67%, and 99.31% over Indian Pines, Salinas, and Pavia University datasets, respectively. Moreover, it is worth mentioning that these excellent results were achieved with comparatively fewer epochs, which also confirms the fast learning capabilities of the SSCRN.

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

Karst is characteristically complex, hydrogeologically, due to a high degree of heterogeneity, which is often typified by specific features, for example, cavities and sinkholes, embedded in a landscape with significant spatial variability of weathering. Characterization of such heterogeneity is challenging with conventional hydrogeological methods, however, geophysical tools offer the potential to gain insight into key features that control the hydrological function of a karst aquifer. Electrical resistivity tomography (ERT) is recognized as the most effective technique for mapping karstic features. This method is typically carried out along transects to reveal 2D models of resistivity variability. However, karstic systems are rarely 2D in nature. In this study, ERT is employed in valley and hillslope regions of a karst critical zone observatory (Chenqi watershed, Guizhou province, China), using a quasi-3D approach. The results from the extensive geophysical surveys show that there is a strong association between resistivity anomalies and known karstic features. They highlight the significance of a marlstone layer in channeling spring flow in the catchment and confining deeper groundwater flow, evidenced by, for example, localized artesian conditions in observation wells. Our results highlight the need to analyze and interpret geophysical data in a three-dimensional manner in such highly heterogeneous karstic environments, and the value of combining geological and hydrogeological data with geophysical models to help improve our understanding of the hydrological function of a karst system.

Classification and segmentation of point clouds have attracted increasing attention in recent years. On the one hand, it is difficult to extract local features with geometric information. On the other hand, how to select more important features correctly also brings challenges to the research. Therefore, the main challenge in classifying and segmenting the point clouds is how to locate the attentional region. To tackle this challenge, we propose a graph-based neural network with an attention pooling strategy (AGNet). In particular, local feature information can be extracted by constructing a topological structure. Compared to existing methods, AGNet can better extract the spatial information with different distances, and the attentional pooling strategy is capable of selecting the most important features of the topological structure. Therefore, our model can aggregate more information to better represent different point cloud features. We conducted extensive experiments on challenging benchmark datasets including ModelNet40 for object classification, as well as ShapeNet Part and S3DIS for segmentation. Both the quantitative and qualitative experiments demonstrated a consistent advantage for the tasks of point set classification and segmentation.

Accurate and comprehensive 3D objects detection is important for perception systems in autonomous driving. Nevertheless, contemporary mainstream methods tend to perform more effectively on large objects in regions proximate to the LiDAR, leaving limited exploration of long-range objects and small objects. The divergent point pattern of LiDAR, which results in a reduction in point density as the distance increases, leads to a non-uniform point distribution that is ill-suited to discretized volumetric feature extraction. To address this challenge, we propose the Foreground Voxel Proposal (FVP) module, which effectively locates and generates voxels at the foreground of objects. The outputs are subsequently merged to mitigating the difference in point cloud density and completing the object shape. Furthermore, the susceptibility of small objects to occlusion results in the loss of feature space. To overcome this, we propose the Multi-Scale Feature Integration Network (MsFIN), which captures contextual information at different ranges. Subsequently, the outputs of these features are integrated through a cascade framework based on transformers in order to supplement the object features space. The extensive experimental results demonstrate that our network achieves remarkable results. Remarkably, our approach demonstrated an improvement of 8.56% AP on the SECOND baseline for the Car detection task at a distance of more than 20 m, and 9.38% AP on the Cyclist detection task.

Research on video smoke detection has become a hot topic in fire disaster prevention and control as it can realize early detection. Conventional methods use handcrafted features rely on prior knowledge to recognize whether a frame contains smoke. Such methods are often proposed for fixed fire scene and sensitive to the environment resulting in false alarms. In this paper, we use convolutional neural networks (CNN), which are state-of-the-art for image recognition tasks to identify smoke in video. We develop a joint detection framework based on faster RCNN and 3D CNN. An improved faster RCNN with non-maximum annexation is used to realize the smoke target location based on static spatial information. Then, 3D CNN realizes smoke recognition by combining dynamic spatial–temporal information. Compared with common CNN methods using image for smoke detection, 3D CNN improved the recognition accuracy significantly. Different network structures and data processing methods of 3D CNN have been compared, including Slow Fusion and optical flow. Tested on a dataset that comprises smoke video from multiple sources, the proposed frameworks are shown to perform very well in smoke location and recognition. Finally, the framework of two-stream 3D CNN performs the best, with a detection rate of 95.23% and a low false alarm rate of 0.39% for smoke video sequences.

3D shape processing is a fundamental computer application. Specifically, 3D mesh could provide a natural and detailed way for object representation. However, due to its non-uniform and irregular data structure, applying deep learning technologies to 3D mesh is difficult. Furthermore, previous deep learning approaches for 3D mesh mainly focus on local structural features and there is a loss of information. In this paper, to make better mesh shape awareness, a novel deep learning approach is proposed, which aims to full-use the information of mesh data and exploit comprehensive features for more accurate classification. To utilize self-attention mechanism and learn global features of mesh edges, we propose a novel attention-based structure with the edge attention module. Then, for local feature learning, our model aggregates edge features from adjacent edges. We refine the network by discarding pooling layers for efficiency. Thus, it captures comprehensive features from both local and global fields for better shape awareness. Moreover, we adopt spatial position encoding module based on spatial information of edges to enhance the model to better recognize edges and make full use of mesh data. We demonstrate effectiveness of our model in classification tasks with numerous experiments which show outperforming results on popular datasets.

As technology evolves, the cost of 3D scanners is falling, which makes 3D computer vision for industrial applications increasingly popular. More and more researchers have started to study 3D computer vision. Point cloud feature descriptors are a fundamental task in 3D computer vision, and descriptors that use spatial features tend to perform better than those without them. Point cloud descriptors can generally be divided into local reference frames-based (LRF-based) and local reference frames-free (LRF-free). The former uses LRFs to provide spatial features to the descriptors, while the latter uses point pair features to provide spatial features. However, the performance of those LRF-based descriptors is more affected by local reference frames (LRFs), and the descriptors with spatial information LRF-free tend to be more computationally intensive because of its point pair combination strategy. Therefore, we propose a strategy named Multi-scale Point Pair Combination Strategy (MSPPCS) that reduces the computation of point pair-based feature descriptors by nearly 70 % while ensuring that the performance of the descriptor is almost unaffected. We also propose a new descriptor, Spatial Feature Point Pair Histograms (SFPPH), which has excellent performance and robustness due to the diverse spatial features used. We critically evaluate the performance of our descriptor on the Bologna dataset, Kinect dataset, and UWA dataset. The experimental results show that our descriptor is the most robust and performing point cloud feature descriptor.