Interferometry Synthetic Aperture Radar (InSAR) provides unique capabilities to map regional/global topography and deformation of the Earth’s surface and has led to a broad spectrum of deformation monitoring applications. In order to adapt to various challenging monitoring environments, researchers have made tremendous innovations to deal with issues such as atmospheric and ionospheric effects, loss of coherence due to large displacements, geometric distortions and unwrapping errors. Owing to recent technical and methodological advances, the Earth’s surface deformation, ranging from earthquake ruptures, volcanic eruptions, landslides, glaciers, to groundwater storage variations, mining subsidence and infrastructure instability can now be mapped anywhere in the world at high spatial and temporal resolutions. This special issue received a set of contributions highlighting recent advances in methodologies and applications of InSAR to ground deformation monitoring. We aim to present overviews of both the state of the art of SAR/InSAR techniques and the next generation of applications across the broad range of deformation monitoring applications.
Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon. In particular, carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products. Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets, including observations acquired by the legendary Landsat mission and new systems launched within the past decade, including Sentinel-2, Sentinel-1, GEDI, and ICESAT-2. With the L-band NISAR and P-band BIOMASS missions to be launched in 2023, the Earth’s land surfaces will be imaged by optical and multi-band (including C-, L-, and P-bands) radar systems that can provide global, sub-weekly observations at sub-hectare spatial resolutions for public use. Fine scale products derived from these observations will be crucial for developing monitoring, reporting, and verification (MRV) capabilities needed to support carbon trade, REDD+, and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels. Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics, this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass, detect forest disturbances, determine disturbance attribution, quantify disturbance intensity, and estimate harvested timber volume. Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products. In addition, two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities. Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.
Cartography and maps support the continuous rising of the awareness of the power of spatial data, which further lays a foundation for the popularity of various location based services and applications in society. Cartography and Geographic Information System education has been a core activity in the cartographic academic community for knowledge creation and transfer in higher education institutions. Maps in primary and high schools play a unique role across disciplines to build the spatial thinking capacities of young generations. Over years educators train students via lectures and lab works into which digital technologies are gradually incorporated. The COVID-19 pandemic has been fast forwarding our pace to employ digital technologies in online teaching and learning. Teachers are passively or proactively adapted to conduct their teaching online and redesign their lectures and assessments of students’ performance. On another side, students are getting used to online learning even more quickly with various digital devices in an interactive and collective way. It creates opportunities for cartographic GIS educators to build a body of knowledge for cartography which can be used to build open source educational resources systematically. Further flexible curriculum can be designed and implemented for professional and continuous education and training at various levels. Future education of cartography and GIS can improve map literacy and make a sustainable education.
Reliable and prompt information on forest above-ground biomass (AGB) and tree diameter at breast height (DBH)are crucial for sustainable forest management. Remote sensing technology, especially the Light Detection and Ranging (LiDAR) technology, has been proven to estimate important tree variables effectively. This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics. In the suggested DBH prediction, we developed a nonlinear estimation equation using the total tree height. As for the AGB prediction approach, we used regression methods such as multiple linear regression (MLR), random forest (RF) and support vector machine for regression (SVR). We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees, located in the Yellow River Delta (YRD), China. For our developed approaches, we used Unmanned Aerial Vehicle (UAV) and Backpack LiDAR point cloud datasets obtained in June 2017, and three field data measurements gathered in June 2017 and 2019 and October 2019, all from the same study area. The results demonstrate that: ① The LiDAR data individual tree segmentation (ITS) from which we extracted individual tree information like tree location and tree height, was carried out with an overall accuracy F=0.91; ② We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error (RMSE)=3.61cm, later validated by the 2017 dataset; ③ Forest AGB at stand level was estimated with the MLR, RF and also SVR regression methods, and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR. Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’ accuracy. Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS. The UAV LiDAR ability to provide high accuracy tree height abstraction, the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB. This study shows that being cost-free is not the only advantage of free available software. In the performance of ITS and the LiDAR, metrics extraction proved to be as good as the commercially available software.
SpacetimeAI and GeoAI are currently hot topics, applying the latest algorithms in computer science, such as deep learning, to spatiotemporal data. Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing, their applications in other spatial and space-time data types are still immature. This paper sets up the proposition of using a network (&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points, polylines, and polygons. We illustrate network and graph-based SpaceTimeAI, from graph-based deep learning for prediction, to space-time clustering and optimisation. These applications demonstrate the advantages of network (graph)-based SpacetimeAI in the fields of transport&mobility, crime&policing, and public health.
LuTan-1 (LT-1) is a constellation with two full-polarization L-band radar satellites designed by China, and the first satellite was scheduled to be launched in December 2021 and the second one in January 2022. The LT-1 will be operated for deformation monitoring in repeat-pass mode, and for DEM generation in bistatic mode, improving self-sufficiency of SAR data for the field of geology, earthquake, disaster reduction, geomatics, forestry and so on. In this paper, we focused on designing an algorithm for interferometric DEM generation using LT-1 bistatic satellites. The basic principle, main error sources and errors control of the DEM generation algorithm of LT-1 were systematically analyzed. The experiment results demonstrated that: ① The implemented algorithm had rigorous resolution with a theoretic accuracy better than 0.03m for DEM generation. ② The errors in satellite velocity and Doppler centroid had no obvious effect on DEM accuracy and they could be neglected. While the errors in position, baseline, slant range and interferometric phase had a significant effect on DEM accuracy. And the DEM error caused by baseline error was dominated, followed by the slant range error, interferometric phase error and satellite position error. ③ To obtain an expected DEM accuracy of 2m, the baseline error must be strictly controlled and its accuracy shall be 1.0mm or better for Cross-Track and Normal-Direction component, respectively. And the slant range error and interferometric phase error shall be reasonably controlled. The research results were of great significance for accurately grasping the accuracy of LT-1 data products and their errors control, and could provide a scientific auxiliary basis for LT-1 in promoting global SAR technology progress and the generation of high-precision basic geographic data.
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.
Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science. There are not only the measuring errors of the endpoints but also the modeling errors between the line segments and the actual geographical features. This paper presents a Brownian bridge error model for line segments combining both the modeling and measuring errors. First, the Brownian bridge is used to establish the position distribution of the actual geographic feature represented by the line segment. Second, an error propagation model with the constraints of the measuring error distribution of the endpoints is proposed. Third, a comprehensive error band of the line segment is constructed, wherein both the modeling and measuring errors are contained. The proposed error model can be used to evaluate line segments’ overall accuracy and trustability influenced by modeling and measuring errors, and provides a comprehensive quality indicator for the geospatial data.
In order to meet the requirements of high-precision vehicle positioning in complex scenes, an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed, non-integrity, attitude, and odometer constraint models. In this model, the robust equivalent gain matrix is constructed by the IGG-Ⅲ method to weaken the influence of gross error, and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment, so as to improve the solution performance of filtering system and realize high-precision position, attitude and velocity measurement when GNSS signal is unlocked. A real test on a road over 600km demonstrates that, in about 100km shaded environment, the fixed rate of GNSS ambiguity resolution in the shaded road is 10% higher than that of GNSS only ambiguity resolution. For all the test, the positioning accuracy can reach the centimeter level in an open environment, better than 0.6m in the tree shaded environment, better than 1.5m in the three-dimensional traffic environment, and can still maintain a positioning accuracy of 0.1m within 10s when the satellite is unlocked in the tunnel scene. The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints, which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.
A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud respectively. It aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral information. The whole flowchart of the method is as follows: Firstly, Gaussian decomposition was applied to fit an echo full-waveform. The parameters associated with the Gaussian function were optimized by LM (Levenberg-Marquard) algorithm. Six and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud, respectively. Secondly, a random forest was selected as the classifier to which the generated features were input. Relief-F was used to rank the weights of all the features generated. Finally, features were input to the classifier one by one according to the weights calculated from feature ranking, where classification accuracies were evaluated. The experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification, with 95.4% overall accuracy, 0.90 kappa coefficient, which outperform the results obtained by a single class of features, no matter whether they were generated from point cloud or waveform data.
Low-Rank and Sparse Representation (LRSR) method has gained popularity in Hyperspectral Image (HSI) processing. However, existing LRSR models rarely exploited spectral-spatial classification of HSI. In this paper, we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization (LRSR-ANR) method for HSI classification. In the proposed method, we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously. The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers (M-ADMM), which converges faster than ADMM. Then to incorporate the spatial information, an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood. Lastly, the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error. Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.
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.
The Global Navigation Satellite Systems (GNSS) broadcast radio signals are continuously at two or more frequencies in the L-band, and the multipath signals from sea surface recorded by off-the-shelf geodetic receivers have been demonstrated they can be used to estimate sea level, using a technology called GNSS multipath reflectometry (GNSS-MR). Before proceeding to estimate reflection parameters, the azimuth range and elevation angle range are needed to be defined, as only with suitable azimuths and elevation angles the sensing zones can be guaranteed on water. So, this study presents an angle dependence analysis method to jointly select the azimuth range and elevation angle range based on wavelet analysis which can describe the non-stationary power of different sinusoidal oscillations changed with elevation angle. The key of this method is to use one grid model to screen the spectrum power of multipath oscillation on different elevation angles and azimuths in this work. Then the elevation angles and the azimuths can be determined by searching grids with greater power. The GPS and GLONASS data of two Multi-GNSS Experiment (MGEX) stations named BRST and MAYG was analyzed and used to retrieve. Firstly, the angle dependence analysis was carried out to determine the elevation range and azimuth range. Secondly, the sea levels were retrieved from individual signals. Finally, the retrievals of individual signals are combined to form a 10-min sea level retrieval series. The RMSEs of the combined retrievals are both less than 15cm. The results show the effectiveness of the selection of angle range based on the angle dependence analysis method.
Tianjin is one of the inland cities with the most severe cases of subsidence hazard in China. The majority of the existing studies have mainly focused on Beijing-Tianjin-Hebei, and little attention has been given to Tianjin. In addition, these existing studies are short-term investigations, lacking long-term monitoring of surface subsidence. In the present study, we use the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor the subsidence process in Tianjin between 2015 and 2020 and reveal its spatial and temporal variation. We divided the 44-view Sentinel-1A image data into three periods to avoid the effect of temporal and spatial decoherence by extracting the surface deformation field in Tianjin. We finally verified the accuracy and reliability of the inversion results using second-order leveling data. Results showed that the correlation coefficient r between the two reached 0.89, and the root mean square error was 4.84mm/y. Obvious subsidence funnels exist in Tianjin, mainly in the towns of Wangqingtuo and Shengfang. These subsidence funnels have a subsidence deformation rate of -136.2mm/y and a maximum cumulative settlement of -346.3mm within the study period. The subsidence area tends to extend to the southwest. The analysis of annual rainfall, groundwater resource extraction, spatial location distribution of industrial areas combined with SBAS-InSAR inversion results indicates that overextraction of groundwater resources is the main cause of land subsidence in the area. Therefore, strict control of groundwater extraction is the main approach to mitigate land subsidence effectively.
The expansion of research and applications of Global Navigation Satellite Systems (GNSS) has revealed the information of reflecting surface in inherent multipath errors. GNSS signals, usually used to measure position, have been demonstrated that they can be used to retrieve water properties including water level, soil moisture, snow depth, and vegetation water content, which are important for climate analysis and water resources monitoring. Reflected GNSS signals with different azimuths can carry information of the corresponding reflecting zone, which means every reflected signal has distinct “signal-to-noise ratio (SNR) characteristics” influenced by specific reflecting zones—and the parameter named “Reflector Height (RH)” deduced from SNR frequency is focused on in this study. Thus, after interpolation of a series of reflector height by coordinates of the footprint, products describing highly detailed terrain over a reflecting footprint can be produced. Data of three GNSS sites in EarthScope Plate Boundary Observatory, named P025, P351 and P101, was used to evaluate the terrain after calculating the terrain slopes and correcting the footprint following the slopes. A comparison of the results with a digital elevation model (DEM) showed that it is possible to retrieve terrain by GNSS-Interferometric Reflectometry (GNSS-IR); and the comparison with terrain slopes from DEMs in previous research also validated its potential.
The laser altimeter loaded on the GaoFen-7(GF-7) satellite is designed to record the full waveform data and footprint image, which can obtain high-precision elevation control points for stereo image. The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls, which can be used to judge whether the laser is affected by the cloud. At the same time, the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability. In this manuscript, a data quality analysis scheme of laser altimetry based on footprint image is presented. Firstly, the cloud detection of footprint image is realized based on deep learning. The fusion result of the model is about 5% better than that of the traditional cloud detection algorithm, which can quickly and accurately determine whether the laser spot is affected by cloud. Secondly, according to the characteristics of footprint image, a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers. Based on the above method, the change of laser spot centroid since GF-7 satellite was put into operation is analyzed, and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.
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.
Synthetic Aperture Radar Interferometry (InSAR) has shown its potential on seismic deformation monitoring since it can achieve the accuracy of centimeter level or even the millimeter level. However, the irregular varieties of ionosphere can induce the additional phase delay on SAR interferometry, restricting its further application in high-precision deformation monitoring. Although several methods have been proposed to correct the ionospheric phase delay on SAR interferometry, the performances of them haven’t been evaluated and compared. In this study, three commonly used methods, including polynomial fitting, azimuth offset and split-spectrum are applied to L’Aquila Earthquake to correct the ionospheric phase delay on two Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite-1 (ALOS-1) images. The result indicates that these three methods can effectively correct the ionospheric phase delay error for SAR interferometry, where the standard deviations of the ionosphere-corrected results have decreased by almost a factor of 1.8 times for polynomial fitting method, 4.2 times for azimuth offset method and 2.5 times for split-spectrum method, compared to those of the original phase. Furthermore, the result of the sliding distribution inversion of the seismic fault shows the best performance for split-spectrum method.
Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period. Terrestrial Laser Scanning (TLS) can collect high density and high accuracy point cloud data in a few minutes as an innovation technique, which provides promising applications in tunnel deformation monitoring. Here, an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed. First, the tunnel orientation is determined using principal component analysis (PCA) in the Euclidean plane. Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m. Next, a z-score method is introduced to detect and remove the outlies. Because the tunnel cross-section’s standard shape is round, the circle fitting is implemented using the least-squares method. Afterward, the convergence analysis is made at the angles of 0°, 30° and 150°. The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner. The results indicate that the proposed methodology achieves an overall accuracy of 1.34mm, which is also in agreement with the measurements acquired by a total station instrument. The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring, which can also be extended to other engineering applications.
Using GNSS-R technology for remote sensing of surface parameters has become a new trend in the field of remote sensing. With the rapid development of GNSS-R technology, GNSS-R simulation has become one of the new hot spots. Now the researches of the GNSS-R simulation are all the simulations that consider a single star or a single frequency point, and in actual applications, the signal captured by the receiver is often the reflected signals of multiple stars. In view of this situation, from the perspective of multi-satellite simulation, this paper gives the model of GNSS-R multi-satellite ocean simulation on the basis of analyzing the remote sensing principle, reflection signal model and simulation principle of GNSS-R technology. Based on the GNSS-R multi-satellite ocean simulation model and the fast parallel computing capability of GPU, the GNSS-R multi-satellite ocean simulator was designed. Finally, the direct and reflected signals generated by the GNSS-R multi-satellite simulator were tested and verified. The results show that the positioning result of the direct signal meets the positioning accuracy requirements; The delay-related power results obtained from the simulated two-satellite reflected signals processing are in good agreement with the theoretical model, and the correlation coefficients are all above 0.99; The generated signals are used for GNSS-R height measurement technology, the height measurement error is about 1.4~1.8m, which is in line with the accuracy of the C/A code ranging receiver; And the parallel operation of the GPU for multi-satellite simulation calculation is 800—900 times higher than the traditional CPU calculation. It proves that the proposed model and the designed simulator are feasible and accurate.
Snow cover is one of the important components of land cover, and it is necessary to accurately monitor the depth and coverage of snow cover. Using the GPS signal receiver data and the basic principle of snow depth detection based on GPS-MR technology, the snow depth of the three sites on the Greenland PBO network GLS1, GLS2, and GLS3 from 2012 to 2018 was obtained. The inversion snow depth is affected by site drift, which is a quite difference from the measured snow depth. Combined with the stable reference point, the velocity field distribution of Greenland Island and the U-direction component change value of the station can be obtained through GAMIT calculation. By analyzing the glacial flow and U-direction component, the influence of the site drift on the snow depth was deducted, and finally compared the corrected inversion snow depth and measured snow depth found that the two were better than before the correction, the results were significantly improved, and the consistency was good. The analysis of the experimental results showed that in extremely cold areas such as Greenland Island, affected by glaciers, the continuous, real-time, high-time resolution snow depth around the measured station obtained by ground-based GPS tracking stations has a large gap with the measured snow depth value, and the gap will gradually increase with time. By deducting the impact of glacier drift, the trend of the two is the same and the consistency is good. The correctness and feasibility of the application of ground-based GPS snow cover theory in the polar area further expand the application scope and practical value of ground-based GPS in snow monitoring.
Earth’s ionosphere is an important medium for navigation, communication, and radio wave transmission. Total Electron Content (TEC) is a descriptive quantify for ionospheric research. However, the traditional empirical model could not fully consider the changes of TEC time series, the prediction accuracy level of TEC data performed not high. In this study, an improved Extreme Learning Machine (ELM) model is proposed for ionospheric TEC prediction. Improvements involved the use of Empirical Mode Decomposition (EMD) and a Fuzzy C-Means (FCM) clustering algorithm to pre-process data used as input to the ELM model. The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy. TEC measurements provided by the Centre for Orbit Determination in Europe (CODE) were used to evaluate the performance of the improved ELM model in terms of prediction accuracy, applicable latitude, and the number of required training samples. Experimental results produced a Mean Relative Error (MRE) and a Root Mean Square Error (RMSE) of 8.5% and 1.39 TECU, respectively, outperforming the ELM algorithm (RMSE=2.33 TECU and MRE=17.1%). The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions, with a mean relative error of 7.6%. This value improved further as the number of available training data increased and when 20-doys data were trained, achieving a mean relative error of 4.9%. These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.
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.
Any single Positioning, Navigation and Timing (PNT) technology has its vulnerability and limits, even the powerful Global Navigation Satellite System (GNSS) is no exception. To provide continuous and reliable PNT information to users, the theory and technique of comprehensive PNT information system and resilient PNT application system have attracted great attention from Chinese scholars. We try to summarize the progress and development of the synthetic PNT system, including the proposal, the modification and the improvement of the comprehensive PNT, as well as the follow-up resilient PNT. The frame of China’s comprehensive PNT system consisted of comprehensive PNT infrastructure and comprehensive PNT application system is initially described; the achievements on some main PNT technologies are introduced; the conceptual models of resilient PNT are given; besides, existing researches on resilient function models and stochastic models are summarized according to different user scenarios.
If geodetic coordinates from an ellipsoid are included in the equations of a projection for mapping a sphere instead of geographical coordinates, the result will be a projection of the ellipsoid into a plane. This will slightly change the distortion distribution of the initial map projection. The question is to what extent the replacement of geographical with geodetic coordinates will affect this change. In this paper, we deal with conformal, equal-area and equidistant projections of the sphere, which we modify by using geodetic coordinates instead of geographical ones. The result will be an approximately conformal, approximately equal-area and approximately equidistant projection. It is shown that in this case the maximum distortion of the angles in approximately conformal projections will be approximately 23.09', the maximum distortion of the area in approximately equal-area projections less than 0.7% and the maximum distortion of lengths in approximately equidistant projections less than 0.7%, therefore on the maps imperceptible.
Nowadays Surveying and Mapping (S&M) production and services are facing some serious challenges such as real-timization of data acquisition, automation of information processing, and intellectualization of service applications. The main reason is that current digitalized S&M technologies, which involve complex algorithms and models as the core, are incapable of completely describing and representing the diverse, multi-dimensional and dynamic real world, as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models. In order to address these challenges, it is necessary to explore the use of natural intelligence in S&M, and to develop intelligentized S&M technologies, which are knowledge-guided and algorithm-based. This paper first discusses the basic concepts and ideas of intelligentized S&M, and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M, the construction and realization of hybrid intelligent computing paradigm, and the mechanism and path of empowering production. Further research directions are then proposed in the four areas, including knowledge systems, technologies and methodologies, application systems, and instruments and equipments of intelligentized S&M. Finally, some institutional issues related to promoting scientific research and engineering applications in this area are discussed.
Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing; GIS: Geographical Information System; GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.
In March 2021, a seismic sequence including three Mw>5.5 events struck northern Thessaly, Greece. Owing to the high temporal resolution of Sentinel-1 images which were sampled every 6 days and recorded the three events separately, we are able to map individually the coseismic deformation fields of the three events. Based on their respective coseismic displacements, we determined the geometry of the fault plane for each earthquake with the method of multipeak particle swarm optimization and inverted the best-fitting slip distribution by linear least squares inversion. Modelling results show that the three events occurred successively on 3, 4 and 12 March 2021 were all dominated by normal-slip motions on previously unknown faults within the top 15km of the Earth’s crust. The 3 March 2021 Mw 6.3 earthquake ruptured a northeast-dipping fault with a strike angle of 301° (clockwise from the North) and a dip angle of 46°, producing the maximum slip of about 2.2m. The slip motion of the 4 March 2021 Mw 5.9 aftershock shows a similar fault geometry (striking 297° and dipping 42°) to the 3 March mainshock, but with a considerably smaller dip-slip component (~0.8m). The 12 March 2021 Mw 5.6 aftershock occurred on a southwest-dipping fault (striking 100° and dipping 40°) with a normal fault slip of up to 0.5m. Static Coulomb stress changes triggered by the earthquake sequence imply a promotion relationship between the first 3 March event and the two subsequent events. Due to the coseismic stress perturbation, more than 70% of aftershocks were distributed in areas with increased Coulomb stress and the northwest segment of the Larissa fault close to the seismic sequence was exposed to a relatively high seismic risk.
Educating the future generation of modern cartographers, being able to deal with the rapid changes of modern technologies and developing the skills and competences to not only being able to cope with those challenges but also to be able to contribute to develop the domain further, has become a rising concern of many. In this paper, the experiences of setting up an International MSc program are shared as well as some reasoning for the development of an underpinning curriculum are given.
Urban public infrastructure is an important basis for urban development. It is of great significance to deepen the research on intelligent management and control of urban public infrastructure. Spatio-temporal information contains the law of state evolution of urban public infrastructure, which is the information base of intelligent control of infrastructure. Due to the needs of operation management and emergency response, efficient sharing and visualization of spatio-temporal information are important research contents of comprehensive management and control of urban public infrastructure. On the basis of summarizing the theoretical research and application in recent years, the basic methods and current situation of the acquisition and analysis of spatio-temporal information, the forecast and early warning, and the intelligent control of urban public infrastructure are reviewed in this paper.
《测绘学报(英文版)》(JGGS)由中国科学技术协会主管,中国测绘学会、中国地图出版社有限公司主办,测绘出版社有限公司出版,是《测绘学报》中文版的姊妹刊,面向国内外发行,季刊。