The geodesy discipline has been evolving and constantly intersecting and merging with other disciplines in the last 50 years, due to the continuous progress of geodetic observation techniques and expansion of application fields. This paper first introduces the development and roles of geodesy and its formation. Secondly, the development status of geodesy discipline is analyzed from the progress of observation techniques and cross-discipline formation is analyzed from the expansion of application fields. Furthermore,the development trend of geodesy is stated from the perspective of national requirements and scientific developments. Finally, the sub-disciplines for geodesy are suggested at the present stage, based on the requirements of the National Natural Science Foundation of China and development status of geodesy itself, which can provide references for topic selection and fund application of geodetic scientific research.

RGB-D camera is a new type of sensor, which can obtain the depth and texture information in an unknown 3D scene simultaneously, and they have been applied in various fields widely. In fact, when implementing such kinds of applications using RGB-D camera, it is necessary to calibrate it first. To the best of our knowledge, at present, there is no existing a systemic summary related to RGB-D camera calibration methods. Therefore, a systemic review of RGB-D camera calibration is concluded as follows. Firstly, the mechanism of obtained measurement and the related principle of RGB-D camera calibration methods are presented. Subsequently, as some specific applications need to fuse depth and color information, the calibration methods of relative pose between depth camera and RGB camera are introduced in Section 2. Then the depth correction models within RGB-D cameras are summarized and compared respectively in Section 3. Thirdly, considering that the angle of the view field of RGB-D camera is smaller and limited to some specific applications, we discuss the calibration models of relative pose among multiple RGB-D cameras in Section 4. At last, the direction and trend of RGB-D camera calibration are prospected and concluded.

National flags are very important symbols of countries. They represent the countries’s authority and dignity of a country. However, there are some occasions where the flags were produced incorrectly(or as frauds) but still hung officially without a formal inspection. In this paper, we propose a photogrammetric inspection method for hanging national flags of China, for which only one single image is required to perform the inspection. The proposed method allows automatic estimation of the relative positions and orientation of the pentagrams, so exposure of inappropriate flags can be identified avoided. The method invokes a novel 2D geometric model of a pentagram (five-pointed star)to constrain an adjustment to estimate the camera’s exterior orientation parameters based on a single image of a statically hung flag. Conventional error parameters such as the radial distortion parameters are integrated into the pentagram model to form a calibration process to reduce the 3D reconstruction errors. Once the camera and the distortion parameters are estimated, the relative positions, orientations, and dimensions of all the five pentagrams can be readily computed with independent pentagram fitting so the flag quality can be verified using the national standard. More than 20 different hanging flags were captured to verify the proposed method. The results indicate that the method is flexible and accurate, with an accuracy of 1.1mm for the position/dimension, and 0.2° for the orientation on average. Since the method is based on the proposed geometric model of the pentagram, it can be readily adapted to form another system to verify other countries’ national flags containing more than one pentagram.

Hyperspectral Image (HSI) classification based on deep learning has been an attractive area in recent years. However, as a kind of data-driven algorithm, the deep learning method usually requires numerous computational resources and high-quality labelled datasets, while the expenditures of high-performance computing and data annotation are expensive. In this paper, to reduce the dependence on massive calculation and labelled samples, we propose a deep Double-Channel dense network (DDCD) for Hyperspectral Image Classification. Specifically, we design a 3D Double-Channel dense layer to capture the local and global features of the input. And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs. The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods, which means DDCD owns simpler architecture and higher efficiency. A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance, even though when the absence of labelled samples is severe.

The gravity anomaly and vertical gravity gradient due to anomalous geological bodies are mainly computed by numerical methods, so it is difficult and time-consuming to use the gravity-geological method to invert seafloor topography. This paper addresses this issue by deriving an expression for gravity generated by a cylinder based on a series expansion. The choice of number for terms in the series is estimated by comparing with the numerical method, especially when the depth H=4000m, the accuracy of 1mGal(1Gal=10^-2m/s²) can be achieved when the series are 9. The expressions can be used to establish the relationships between the shape of an anomalous body and the generated vertical gravity and vertical gravity gradient, respectively. Finally, the potential applications of the expressions in inverting seafloor topography are illustrated by synthetic examples.

The magnetic compensation of aeromagnetic survey is an important calibration work, which has a great impact on the accuracy of measurement. In an aeromagnetic survey flight, measurement data consists of diurnal variation, aircraft maneuver interference field, and geomagnetic field. In this paper, appropriate physical features and the modular feedforward neural network (MFNN) with Levenberg-Marquard (LM) back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data. LM algorithm is a kind of least square estimation algorithm of nonlinear parameters. It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method. LM algorithm’s computing efficiency is high and fitting error is very low. The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON (T-L) model with the linear least square (LS) solution by fitting experiments with five different aeromagnetic surveys’ data.

As an independent navigation method, inertial navigation system(INS) has played a huge advantage in a lot of special conditions. But its positioning error will accumulate with time, so it is difficult to work independently for a long time. The vehicle loaded with the inertial navigation system usually drives on the road, so the high precision road data based on geographic information system(GIS) can be used as a bind of auxiliary information, which could correct INS errors by the correlation matching algorithm. The existing road matching methods rely on mathematical models, mostly for global positioning system(GPS) trajectory data, and are limited to model parameters. Therefore, based on the features of inertial navigation trajectory and road, this paper proposes a road data aided vehicle inertial navigation method based on the learning to rank and iterative closest contour point(ICCP) algorithm. Firstly, according to the geometric and directional features of inertial navigation trajectory and road, the combined feature vector is constructed as the input value; Furthermore, the scoring function and RankNet neural network based on the features of vehicle trajectory data and road data are constructed, which can learn and extract the features; Then, the nearest point of each track point and its corresponding road data set to be matched is calculated. The average translation between the two data sets is calculated by using the position relationship between each group of track points to be matched and road points; Finally, the trajectory data set is iteratively translated according to the translation amount, and the matching track point set is obtained when the trajectory error converges to complete the matching. During experiments, it is compared with other algorithms including the hidden Markov model(HMM) matching method. The experimental results show that the algorithm can effectively suppress the divergence of trajectory error. The matching accuracy is close to HMM algorithm, and the computational efficiency can meet the requirements of the traditional matching algorithm.

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models. The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency, so it is widely used in close-range, aerial and satellite image matching. Based on the analysis of the original semi-global matching algorithm, this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture.
The method includes 4 parts: ① Precise orientation. Aiming at the system error in the image orientation model, the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images, and the sub-pixel positioning accuracy is obtained; ② Epipolar image generation. After the original image is divided into blocks, the epipolar image is generated based on the projection trajectory method; ③ Image dense matching. In order to reduce the size of the cost space and calculation time, the image is pyramided and then semi-globally matched layer by layer. In the matching process, the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects; ④ Generate DSM. In order to test the matching effect, the weighted median filter algorithm is used to filter the disparity map, and the DSM is obtained based on the principle of forward intersection. Finally, the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.

This paper first introduces the background and basic concept of digital twin city, then analyzes the relationship between digital twin city and smart city. Next, it introduces the primary supporting technologies for the construction of a digital twin city, and finally summarizes the current application status and development trends regarding digital twin city. The authors argue that digital twin technology will face challenges in regards to data, basic knowledge base, system integration, and talent issues if it is to be more widely applied in the construction of the smart city. Additionally, the authors propose institutional and technical suggestions for solving these problems at the macro and micro levels.