Taking autonomous driving and driverless as the research object, we discuss and define intelligent high-precision map. Intelligent high-precision map is considered as a key link of future travel, a carrier of real-time perception of traffic resources in the entire space-time range, and the criterion for the operation and control of the whole process of the vehicle. As a new form of map, it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps. Thus, it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map. Accordingly, we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application. Next, we put forward the data logic structure of intelligent high-precision map, and analyze its application in autonomous driving. Then, we summarize the computing mode of “Crowdsourcing+Edge-Cloud Collaborative Computing”, and carry out key technical analysis on how to improve the quality of crowdsourced data. We also analyze the effective application scenarios of intelligent high-precision map in the future. Finally, we present some thoughts and suggestions for the future development of this field.

Target detection technology of synthetic aperture radar (SAR) imageis widely used in the field of military reconnaissance and surveillance. The traditional SAR image target detection methods need to be provided a lot of empirical knowledge because the characteristics of SAR images in different configurations (attitude, pitch angle, imaging parameters, etc.) will change greatly,resulting in high generalization error. Currently, deep learning method has achieved great success in the field of image processing. Research shows that deep learning can achieve a more intrinsic description of the data, while the model has a stronger ability of modeling and generalization. In order to solve the problem of insufficient data in SAR data sets, an experimental system for acquiring SAR image data in real scenes was built. Then the transfer learning method and the improved convolution neural network algorithm (PCA+Faster R-CNN) are applied to improve the target detection precision. Finally, experimental results demonstrate the significant effectiveness of the proposed method.

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional (3D) laser scanning technology. Due to the fragmentation and irregularity of the surface morphology, when applying the 3D laser scanning technology to mountain mapping, the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications. In order to solve this problem, we propose to repair the valley and ridge line first, and then repair the point cloud hole. The main technical steps of the method include the following points: First, the valley and ridge feature lines are extracted by the GIS slope analysis method; Then, the valley and ridge line missing from the hole are repaired by the mathematical interpolation method, and the repaired results are edited and inserted to the original point cloud; Finally, the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired. Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method. The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software, the average repair accuracy of the proposed method, in the 16m buffer zone of valley line and ridge line, is increased from 56.31cm to 31.49cm. The repair performance is significantly improved.

Currently, deep convolutional neural networks have made great progress in the field of semantic segmentation. Because of the fixed convolution kernel geometry, standard convolution neural networks have been limited the ability to simulate geometric transformations. Therefore, a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation. Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture. To overcome this shortcoming, the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation. The proposed method can easily be trained by end-to-end using standard backpropagation algorithms. Finally, the proposed method is tested on the ISPRS dataset. The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

The solid Earth responds elastically to terrestrial water storage (TWS)changes. Here global positioning system (GPS) vertical position data at 31 stations from the crustal movement observation network of China (CMONOC) from August 2010 to December 2016 are used to detect droughts in Southwest China. Monthly GPS vertical position displacements respond negatively to precipitation changes and TWS changes observed by gravity recovery and climate experiments(GRACE) as well as river water level variations. GPS vertical position anomalies (the non-seasonal term) are well correlated negatively (correlations of about -0.70) with the commonly used meteorological composite index (CI) in China and the GRACE drought severity index (GRACE-DSI),but less correlated with the standardized precipitation evapotranspiration index (SPEI). Compared to CI, GPS vertical position anomalies have the advantage of detecting droughts caused by abrupt precipitation deficits in a short time. GRACE-DSI is less accurate in drought monitoring for some periods due to the missing data, while the severity of abrupt precipitation absent in some cases can be overestimated from SPEI with big variability. This study shows the reliability and advantages of GPS data in drought monitoring.

In surveying adjustment models, there is usually some uncertain additional information or prior information on parameters, which can constrain the parameters, and guarantee the uniqueness and stability of parameter solution. In this paper, we firstly use ellipsoidal sets to describe uncertainty, and establish a new adjustment model with ellipsoidal uncertainty. Furthermore, we give a new adjustment criterion based on minimization trace of an outer ellipsoid with two ellipsoid intersections, and analyze the propagation law of uncertainty. Correspondingly, we give a new algorithm for the adjustment model with ellipsoid uncertainty. Finally, we give three examples to test and verify the effectiveness of our algorithm, and illustrate the relation between our result and the weighted mixed estimation.

In view of the limitation of the difference method, the adjustment model of CPⅢ precise trigonometric leveling control network based on the parameter method was proposed in the present paper. The experiment results show that this model has a simple algorithm and high data utilization, avoids the negative influences caused by the correlation among the data acquired from the difference method and its accuracy is improved compared with the difference method. In addition, the strict weight of CPⅢ precise trigonometric leveling control network was also discussed in this paper. The results demonstrate that the ranging error of trigonometric leveling can be neglected when the vertical angle is less than 3 degrees. The accuracy of CPⅢ precise trigonometric leveling control network has not changed significantly before and after strict weight.

The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance, while it is lack of taking the contour geometric features into account, which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes. In light of this, it is put forward that a matching strategy from coarse to precious based on the contour geometric features. The proposed matching strategy can be described as follows. Firstly, the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector. Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution. Accordingly, the identical contours could be matched based on the above calculated results. In the experiment for the proposed method, the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively. It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.

In view of the TIN_DDM buffer surface existing in the construction and application of special data type, algorithm efficiency and precision are not matching; the paper applied the rolling ball model in the process of TIN_DDM buffer surface construction. Based on the precision limitation analysis of rolling ball model, the overall precision control method of rolling ball model has been established. Considering the efficiency requirement of TIN_DDM buffer surface construction, the influence principle of key sampling points and rolling ball radius to TIN_DDM buffer surface construction efficiency has been elaborated, and the rule of identifying key sampling points has also been designed. Afterwards, by erecting the numerical relationship between key sampling points and rolling ball radius, a TIN_DDM buffer surface construction algorithm based on rolling ball acceleration optimization model has been brought forward. The time complexity of the algorithm is O(n). The experiments show that the algorithm could realize the TIN_DDM buffer surface construction with high efficiency, and the algorithm precision is controlled with in 2σ.

The traditional spheroidal kernel results in the spectrum leakage, and the utilization rate of the removed degrees of the measured data is low. Hence, a kind of spheroidal kernel whose high- and low-degrees are both modified is introduced in this research, which is exampled by the Hotine kernel. In addition, the low-degree modified spheroidal kernel is proposed. Either cosine or linear modification factors can be utilized. The modified kernel functions can effectively control the spectrum leakage compared with the traditional spheroidal kernel. Furthermore, the modified kernel augments the contribution rate of the measured data to height anomaly in the modified frequency domain. The experimental results show that the accuracy of the quasi-geoid by the cosine or linear low-degree modified kernel is higher than that by the traditional spheroidal kernel. And the accuracy equals the accuracy of the quasi-geoid using the spheroidal kernel with high- and low-degrees modified approximately when the low-degree modification bandwidths of these two kinds of kernels are the same. Since the computational efficiency of the low-degree modified kernel is much higher, the low-degree modified kernel behaves better in constructing the (quasi-) geoid based on Stokes-Helmert or Hotine-Helmert boundary-value theory.