A solution is introduced in which, instead of providing an object with all information, some of the information is provided by means of one or more group definitions. This is achieved by performing a grouping operation during which an object having object data defining at least one property of the object is assigned to a group having at least one group definition defining at least one feature shared by objects assigned to the group; and storing as mapping information a grouping time indicating the time the grouping operation was performed to the object and a link between the object and the group.

Computer-aided modeling

As an important method for improving dam foundations, curtain grouting is designed to create a hydraulic barrier to decrease permeability, enhance strength, and reduce deformability of rock masses. To evaluate the improvement of rock masses, the Lugeon value (LU), rock quality designation (RQD), and fracture filled rate (FFR) after grouting are key evaluation indicators of grouting efficiency. A prediction method based on an adaptive neuro-fuzzy inference system is proposed to predict and evaluate curtain grouting efficiency in this study. Geological factors (fracture intensity, LU, and RQD before grouting), effective grouting operation factors (effective grouting pressure, effective grouting time, effective grout volume, and effective cement take), and tested interval depth are considered to be the critical factors that greatly influence the efficiency of curtain grouting and are selected as input parameters for prediction models. The grouting efficiency evaluation indicators (the LU value, RQD, and FFR after grouting) are selected as output parameters for evaluation of the efficiency. In addition, a formula for estimating the influence radius of grouting boreholes, which is used to determine the sphere of grouting influence, is proposed. To better reflect the influence of the position of grouting boreholes on the effects of grouting, this study suggests that the effective grouting operation factors can be calculated using an improved inverse distance weighting method. As a case study, this approach is used to predict the results of grouting and to evaluate the efficiency of curtain grouting in hydropower project A, located in the southwestern part of China. The approach shows considerable accuracy in predicting the results of grouting and evaluating grouting efficiency.

Prediction of curtain grouting efficiency based on ANFIS

LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification.

A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet

With the rapid development of remote sensing technology, the quantity and variety of remote sensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remote sensing image recommendation, which can offer related image products to users according to their preference. Although multiple studies on remote sensing retrieval and recommendation have been performed, most of these studies model the user profiles only from the perspective of spatial area or image features. In this paper, we propose a spatiotemporal recommendation method for remote sensing data based on the probabilistic latent topic model, which is named the Space-Time Periodic Task model (STPT). User retrieval behaviors of remote sensing images are represented as mixtures of latent tasks, which act as links between users and images. Each task is associated with the joint probability distribution of space, time and image characteristics. Meanwhile, the von Mises distribution is introduced to fit the distribution of tasks over time. Then, we adopt Gibbs sampling to learn the random variables and parameters and present the inference algorithm for our model. Experiments show that the proposed STPT model can improve the capability and efficiency of remote sensing image data services.

A Space-Time Periodic Task Model for Recommendation of Remote Sensing Images

Three dimensional (3D) geological model is frequently used to represent the geological conditions of the subsurface. The generalized triangular prism (GTP) model designed for borehole sampling data is a spatial data model that could retain the internal connection between the three adjacent boreholes and distinguish between the bedding and cross-bedding directions, which is proper for accurate 3D geological modeling. The traditional building method cannot consider two factors: the borehole distance is usually longer than the stratigraphic thickness, and the top and the bottom surface have different accuracy at the same time. In this study, we describe the new interpolation method for the GTP 3D geological model to improve the model accuracy with sparse borehole data. Firstly, definition and calculation method of the GTP model smoothness are proposed to measure the model smoothness and accuracy degree, which are used to decide whether the GTP voxel requires interpolation. Secondly, the virtual borehole design and calculation method for the GTP voxel subdivision in terms of the GTP geometric smoothness are discussed in detail. Finally, the GTP adaptive interpolation can be performed through the GTP voxel subdivision and the geometric optimization rebuilding. This method could adaptively interpolate the existing GTP model by local updating without changing the GTP model structure, it has high efficiency compared to the classical method. In addition, the feasibility and accuracy of this method could be proven by the actual case. The study will provide a new and reliable interpolation method for the GTP model, and it is also conducive to economic geology related research.

Adaptive Interpolation Method for Generalized Triangular Prism (GTP) Geological Model Based on the Geometric Smoothness Rule

It is an efficient way of directly measured exterior orientations using integrated GPS/IMU system. The experiment has shown that the accuracy performance of direct orientation measurement is sufficient to make orthophoto and small or medium scale map. Unfortunately, since the GPS/IMU orientation module is physically separated from the camera to be oriented translational offsets and rotations are existent and have to be considered. This paper firstly discusses the integrated GPS/IMU system and the elements of GPS/IMU supported aerial photogrammetry. Finally, the overall system calibration is investigated through the data obtained from Fengning, southwest China.

Evaluation of GPS/ IMU Supported Aerial Photogrammetry

3D integral modeling of geology-excavation entities is an important issue of 3D GIS. Based on the past research results of Generalized Tri-Prism (GTP), its modeling object, constituent primitives and topological description mode are extended and amended. According to the extended-GTP model, 8 kinds of geometric primitives and 6 kinds of geological primitives are abstracted from geology-excavation entities, and 3D integral model of geology-excavation entities is put forward. Considering the characteristics of borehole data and GTP modeling process, the 3D constructing inference rule of geological model, which can solve referencing and automatic modeling problem of complex geological entities like bifurcation, pinch-out and fault, is detailed studied. In order to ensure the uniqueness, liability and local operation of converting from GTP into tetrahedron, the Smallest Vertex Identifier (SVID) method is proposed. An experimental system of 3D integral modeling of geology-excavation entities is developed and practically applied in Central Business District (CBD) in Beijing.

A 3D Integral Data Model for Subsurface Entities Based on Extended GTP

Light detection and ranging is a relatively new technology, developed to complement traditional remote sensing technology. Digital aerial image provides reflectance information for land cover while LIDAR data provide more accurate geometrical information. There is much potential for fusing these two sources to achieve increased accuracy and utility for feature detection and classification. In order to assist Guangxi Electric Power Design Institute to improve their efficiency in power transmission line selection and design, field survey work, and reduce the cost, we have recently carried out the first power line project by using LIDAR and high resolution digital camera in China. The use of coloring LIDAR points by RGB of digital images makes it possible for obtaining high quality of in-room surveying, feature detection and classification. In addition, high quality and high resolution of DEM, DSM, DOM and visualization provides our customer not only with better selection and design of power line route, and reduced survey work, but also significant savings by 10 percent reduction of power line construction.

Lidar application in selection and design of power line route

Shaft is the jaws of coal mines. Shaft water flood serious threatens the safety of the shaft. Water sealing by grouting (WSG) is one of the most effective ways to prevent and control shaft and underground-water flood for coal mine. In this paper, a scheme of water sealing by grouting (WSG) with organic material with high water content was designed for the alluvium strata grouting and the fracture strata grouting. By applying the basic principle of neural network (NN), a neural network-based system of grouting quantity prediction was set up. The intelligent prediction of grouting quantity was carried out. The NN predicted data and the data came from field observation were compared. The results show that the NN predicted grouting quantity is close to the real consumption of grouting quantity. And the consumption of the organic material with high water content (OMHWC) is reduced. The study has a certain realistic instructing meaning.

Neural network-based experimental study on shaft water sealing by grouting

Building dismantling bring us many problems in the joint parts of urban and suburb regions. How to estimate house displacement cost is important for real estate before they come in. In this paper the author propose an approach to use aerial orthoimage to obtain the GIS needed vector layer information combined with attribute information which is obtained by field investigation and office collection. Considering the dismantling Index, we can estimate the displacement cost. The experiment results show the method is easy to implement. And the blindness is able to avoid.

Estimation of House Displacement Cost Based on Aerial Orthoimage in Suburb Real Estate GIS

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