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.
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.
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.
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.
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.
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.
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.
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.
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.
In the current era of digital surveying and mapping to intelligent surveying and mapping, ubiquitous surveying and mapping has brought many opportunities and challenges to college engineering course teaching. With the development of ubiquitous surveying and mapping, college engineering practice courses urgently need to respond to ubiquitous surveying and mapping. The research aims to integrate the development of ubiquitous surveying and mapping into the teaching of engineering practice courses in colleges, including promoting Android, Brower/Server (B/S), and Client/Server (C/S) to build a platform for practice courses. This also incorporates real development cases in measurement data processing such as gravity field refinement. In this way, the teaching level of engineering practice courses in colleges can be improved, and new ideas can be put forward for cultivating surveying and mapping talents in the new era in colleges. Finally, it can also provide new ideas for the organization of surveying and mapping practice courses under the background of the pandemic.
Maps have long been a part of everyday life for the general public, and even more so in today’s knowledge society. No doubt, cartography as a profession of map design is assuming a more important role in the formation of intellectual skills in terms of spatial reasoning. Since its emergence as an academic discipline about 100 years ago, cartography has undergone many paradigm shifts. Its interaction with other disciplines has also constantly unfolded. These changes have left traces in cartographic education programs. In the age of big data, however, we are facing four fundamental challenges: (1) cartographic courses are being marginalized or even disappearing from degree programs in geospatial sciences; (2) the role of cartographers is increasingly eclipsed as a side effect of participatory cartography; (3) cartographers are blamed whenever something goes wrong with map use; and (4) professional map publishers can hardly compete with online mapping platforms dominated by Internet giants. Based on a contextual analysis of this seemingly gloomy situation, the paper reveals a number of proliferation points for the design of future cartographic curricula. First, cartography, once dedicated to supporting geospatial sciences, is thriving in the soil of data science, mapping not only the earth or other celestial bodies, but literally any kind of virtual space. Second, cartography has benefited from theoretical and technological advances in cognitive sciences, especially non-intrusive user studies, so that spatial cognition is becoming an integral component of cartographic education. Third, the role of scapegoat for wrongdoing of maps has accentuated cartographer’s overarching responsibility for quality and ethical issues in the geodata value chain. Finally, the diversification of the labor market requires new approaches to prepare future talents for a coopetition-oriented ecosystem in the marketplace.
New requirements have been proposed for GIS practice teaching in colleges and universities in response to the developmental changes of national and industrial sectors during the social transition. Meanwhile, the underlying core characteristics of GIS should remain unchanged in GIS teaching to ensure they serve as the inherent attributes distinguishing GIS from other disciplines. Therefore, the clarification of the dialectical relationship between “changing” and “unchanging” in GIS practice teaching becomes the primary issue to address in relevant teaching reform. To address this issue, the present study systematically analyzes the structural contradictions in GIS practice teaching in the social transition period, and then closely examines the dialectical relationship between “changing” and “unchanging” from the key aspects of educational philosophy, teaching content, teaching methodology, and teaching assessment. Next, using the course of “GIS Practice Design” at the Central South University as an example, the present study describes this university’s reform and inheritance in GIS practice teaching, aiming to provide reference for GIS practice teaching in other universities or majors.
As a consumed and influential natural plant beverage, tea is widely planted in subtropical and tropical areas all over the world. Affected by (sub) tropical climate characteristics, the underlying surface of the tea distribution area is extremely complex, with a variety of vegetation types. In addition, tea distribution is scattered and fragmentized in most of China. Therefore, it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods. This study proposed a boundary-enhanced, object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data. This method uses multispectral indexes, textures, vegetable indices, and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations. To reduce feature redundancy and computation time, the feature elimination algorithm based on Mean Decrease Accuracy (MDA) was used to generate the optimal feature set. Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types, high resolution GF-2 image was segmented based on the MultiResolution Segmentation (MRS) algorithm to assist the segmentation of Sentinel-2, which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations. Finally, the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain, Yunnan Province. The resulting tea plantation map had high accuracy, with a 95.38% overall accuracy and 0.91 kappa coefficient. We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.
In this study, the problem of bundle adjustment was revisited, and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment. The innovation points are reflected in the following aspects: ① The proposed algorithm is not dependent on the Schur complement, and the calculation process is simple and clear; ② The complexities of time and space tend to O(n) in the context of world point number is far greater than that of images and cameras, so the calculation magnitude and memory consumption can be reduced significantly; ③ The proposed algorithm can carry out self-calibration bundle adjustment in single-camera, multi-camera, and variable-camera modes; ④ Some measures are employed to improve the optimization effects. Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness, and it has a strong adaptability as well, because the optimized results are accurate and robust even if the initial values have large deviations from the truth. This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry, computer vision and robotics.
The accuracy of Digital Surface Models (DSMs) generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images. It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced, in which multiple DSMs are combined through computational approaches into a single, more accurate, and complete DSM. However, accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs. Therefore, we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points. The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.
Geomatics is an interdisciplinary subject. Many disciplines have teaching demands in this field. A new course on “Geomatics Technology” has been suggested by the Weiyang College of Tsinghua University of China for the major of “Mathematical and Scientific Basic Science+Civil, Hydraulic and Marine Engineering”. This paper offers a data-led geomatics teaching mode, developing a customized teaching cloud platform, to explore the cross-integrated innovative teaching methods. Teachers and students can assign and submit assignments on this platform. The platform constitutes a data flow with the data download, data processing and result sharing. It encourages communication among students in various majors, grades and units using data as the medium, from data processing to application upstream and downstream. In the “Geomatics Technology” course, geospatial data has emerged as a vital element of the multidisciplinary approach. This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management. Furthermore, the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering, to offer a novel idea for the reform of courses linked to geospatial informatics.
The delimitation of urban development boundaries plays an important role in optimizing the nation land space. “Double evaluation” is one of the important means to study and predict the scale of new construction land in the future and to determine the spatial distribution of urban construction land. This study combines the “double evaluation” with the FLUS (Future Land-Use Simulation) model to study the delimitation of the urban development boundary of Yichang. The results show that: ① the “double evaluation” method comprehensively considers the carrying capacity of the resource environmental bear and the suitability of urban development; ② the FLUS model can better couple the “double evaluation” method for Land Use/Land Cover (LULC) suitability evaluation, Land Use/land Cover Change (LUCC) simulation and urban development boundary delineation, and the overall accuracy of the simulation reaches 96%; ③ according to the requirements of relevant national policies, this study divides the urban development boundary of the study area into concentrated construction areas, elastic development areas and special purpose areas. This function-based division can meet the requirements of urban flexible development, ecological protection and urban safety. This research combines the FLUS model, which is widely used in the simulation of LUCC, with the double evaluation method used in China’s new round of land and space planning to obtain the result of the urban development boundary. This result is consistent with the existing plan of the study area.
Gravity Anomaly Correction (GAC) is a very important term in leveling data processing. In most cases, it is troublesome for field surveyors to measure gravity when leveling. In this paper, based on the complete Bouguer Gravity Anomaly(BGA) map of WGM2012, the feasibility of replacing in-situ gravity surveying in China is investigated. For leveling application, that is to evaluate the accuracy of WGM2012 in China. Because WGM2012 is organized with a standard rectangle grid, two interpolation methods, bilinear interpolating and Inverse Distance Weighted (IDW) interpolating, are proposed. Four sample areas in China, i.e., Hanzhong, Chengdu, Linzhi and Shantou, are selected to evaluate the systems bias and precision of WGM2012. Numerical results show the average system bias of WGM2012 BGA in west China is about -100.1mGal (1mGal=10-5m/s2) and the standard deviation is about 30.7mGal. Tests in Shantou indicate the system bias in plain areas is about -130.4mGal and standard deviation is about 6.8mGal. All these experiments means the accuracy of WGM2012 is limited in high mountain areas of western China, but in plain areas, such as Shantou,WGM2012 BGA map is quite good for most leveling applications after calibrating the system bias.
A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method. The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline. Due to the differences in satellite sensors when producing images, subtle but inherent stripes can appear at the stitching positions between the sensors. These stitchingstripes cannot be eliminated by conventional relative radiometric calibration. The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation, classification and interpretation of remote sensing images. Therefore, a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper. First, the inconsistency in grayscales around stripes is eliminated with the statistical method. Second, the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality. Finally, the details of the images are highlighted by a new image enhancement method, which makes the whole image clearer. Comprehensive experiments are performed, and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.
Convolutional Neural Networks (CNNs) have recently attracted much attention in the ship detection from Synthetic Aperture Radar (SAR) images. However, compared with optical images, SAR ones are hard to understand. Moreover, due to the high similarity between the man-made targets near shore and inshore ships, the classical methods are unable to achieve effective detection of inshore ships. To mitigate the influence of onshore ship-like objects, this paper proposes an inshore ship detection method in SAR images by using hybrid features. Firstly, the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images. Then, a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water. On this basis, the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left. Finally, to verify the effectiveness of the proposed method, numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.
Geodetic functional models, stochastic models, and model parameter estimation theory are fundamental for geodetic data processing. In the past five years, through the unremitting efforts of Chinese scholars in the field of geodetic data processing, according to the application and practice of geodesy, they have made significant contributions in the fields of hypothesis testing theory, un-modeled error, outlier detection, and robust estimation, variance component estimation, complex least squares, and ill-posed problems treatment. Many functional models such as the nonlinear adjustment model, EIV model, and mixed additive and multiplicative random error model are also constructed and improved. Geodetic data inversion is an important part of geodetic data processing, and Chinese scholars have done a lot of work in geodetic data inversion in the past five years, such as seismic slide distribution inversion, intelligent inversion algorithm, multi-source data joint inversion, water reserve change and satellite gravity inversion. This paper introduces the achievements of Chinese scholars in the field of geodetic data processing in the past five years, analyzes the methods used by scholars and the problems solved, and looks forward to the unsolved problems in geodetic data processing and the direction that needs further research in the future.
A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias (SCB). Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server (IGS) server. The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models, with the training time being much shorter than the wavelet neural network model.
The extraction of water bodies is essential for monitoring water resources, ecosystem services and the hydrological cycle, so analyzing water bodies from remote sensing images is necessary. The water index is designed to highlight water bodies in remote sensing images. We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images. Firstly, we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction. Subsequently, we apply KT transformation, LBV transformation, AWEInsh, and HIS transformation to the preprocessed image to calculate a new water index. Then, we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately. Meanwhile, we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies. Finally, we combine small and large water bodies to get complete water bodies. Compared with other traditional methods, our method has apparent advantages in water extraction, particularly in the extraction of small water bodies.
Augmented Reality Geographic Information System (ARGIS) applications can only provide users accurate content services with a highly precise geo-registration. However, the absolute 6DOF (Degree of Freedom) pose provided by the portable sensors is usually inaccurate in urban outdoors, resulting in poorly geo-registration accuracy for ARGIS applications. Aiming at this issue, an automatic vision-aided localization method based on the 2D map is proposed to improve the initial localization accuracy of the portable sensors, and an overall geo-registration optimization framework for outdoor ARGIS is proposed. Based on the initial pose provided by the sensors, the basic principles of the vision-aided localization method are expounded in detail. The experimental results show that the proposed method can effectively correct the initial pose obtained by the pose sensors, and improve the geo-registration accuracy of outdoor ARGIS applications ultimately.
Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers, safe operation of power grids, and sustainable development of cities. However, current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples, and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas. In this study, we use the Seasonal-Trend decomposition procedure based on Loess (STL) based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang, and analyze the relationship between spatial variation and urban functions through Geodetector. The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity, and the abnormal electricity users are mainly located in areas with highly mixed residential, commercial and entertainment functions in the city. The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.
Coastal subsidence monitoring typically employs Global Navigation Satellite System (GNSS) positioning technology. This method provides information only about subsidence below the station base. Sediments in coastal areas tend to accumulate quickly, and subsidence can change significantly due to compaction and alluvium. Therefore, monitoring subsidence above the base is essential to obtain overall coastal subsidence. A new technology called GNSS-Interferometric Reflectometry (GNSS-IR) has been recently developed, which can utilize multipath effects to monitor reflector height. Since the base of the GNSS station is deep and the base length remains constant, the height changes measured by the GNSS-IR technology can reflect subsidence above the base. Accordingly, this paper employs GNSS-IR technology to measure subsidence changes above the base. Additionally, GNSS positioning technology is used to obtain subsidence changes below the base, and the overall subsidence change is then calculated using both GNSS-IR and GNSS positioning technology. The Mississippi River Delta, known for its significant sediment thickness, was selected as the study area, and data from FSHS, GRIS, and MSIN stations was analyzed. The results demonstrate that GNSS-IR can be used to measure the subsidence rate above the base, and the corrected overall subsidence rate is equivalent to the relative sea level rise rate.
With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites, they have become an important support for obtaining global accurate surveying and mapping remote sensing information. At present, optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities, but there is a serious lag problem in mapping and remote sensing information services. It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services. Firstly, based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics, this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites. Further, according to the application requirements of real-time, intelligence, and popularization, puts forward the design concept of integrated intelligent remote sensing satellite integrating communication, navigation, and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite. Then expounds on the performance and characteristics of the Luojia-3 01 satellite, a new generation of intelligent surveying and mapping remote sensing scientific test satellite. And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites. Luojia-3 01 satellite integrates remote sensing and communication functions. It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet, which is of great significance to the construction of China’s spatial information network.
Spatial-temporal analysis of emotions in society has become popular in many studies integrating geography with the humanities, and has shown its influence on social sensing and geo-computation for social sciences. Emotions in society are often volatile, irrational, and vulnerable to the social environment. A critical challenge is to analyze changes in long-term and large-scale emotions in society. In this paper, we propose exploiting this challenge by using spatial-temporal analysis. After extracting emotional, temporal, and spatial information, a spatial standardization approach based on adataset of administrative district changes addresses the problem of Chinese toponym changes. Finally, over 1.7 million news data from the People’s Daily from 1956 to 2014 were collected to explore the changes, spatial distribution, and driving factors of emotions in society using spatial-temporal analysis. The experimental results found that the spatial-temporal analysis of emotions in society in the news is consistent with the results of related sociological research.
Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers, managers, and policymakers in terms of better managing and utilizing their agricultural resources. Due to their small size, irregular shape, and the use of mixed-cropping techniques, the farm fields of smallholder can be difficult to delineate automatically. In recent years, numerous studies on field contour extraction using a deep Convolutional Neural Network (CNN) have been proposed. However, there is a relative shortage of labeled data for filed boundaries, thus affecting the training effect of CNN. Traditional methods mostly use image flipping, and random rotation for data augmentation. In this paper, we propose to apply Generative Adversarial Network (GAN) for the data augmentation of farm fields label to increase the diversity of samples. Specifically, we propose an automated method featured by Fully Convolutional Neural networks (FCN) in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution (VHR) images. We first investigate four State-Of-The-Art (SOTA) FCN architectures, i.e., U-Net, PSPNet, SegNet and OCRNet, to find the optimal architecture in the contour detection task of smallholder farm fields. Second, we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection. We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria. The best combination achieved F1 scores of 0.686 on Test Set 1 (TS1), 0.684 on Test Set 2 (TS2), and 0.691 on Test Set 3 (TS3). Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task. The conceptual, theoretical, and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.
《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。