Table of Content

20 September 2021, Volume 50 Issue 9

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Smart Surveying and Mapping

Progress and challenges of geospatial artificial intelligence

ZHANG Yongsheng, ZHANG Zhenchao, TONG Xiaochong, JI Song, YU Ying, LAI Guangling

2021, 50(9):  1137-1146.  doi:10.11947/j.AGCS.2021.20200420

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With the rapid development of geospatial science, artificial intelligence, and high-performance computing, geospatial artificial intelligence has become the major technique for processing and analyzing geospatial big data, and will be widely used in the scientific research and engineering application of earth science, spatial cognition, and smart city. Geospatial intelligence, as an inter-discipline between geospatial science and artificial intelligence, is driven by dual disciplines. At present, it has made important progress in hardware research, system development, data and model sharing, services and applications, and is also facing new challenges. This paper first describes the conceptual evolution of geospatial artificial intelligence and lists the frameworks of some technical systems. The paper then reviews the state-of-the-art in the scientific research and typical application domains. The problems and challenges facing geospatial intelligence are analyzed. Finally, some future prospects and trends are presented.

Technology and applications of dynamic and precise engineering surveying

LI Qingquan, ZHANG Dejin, WANG Chisheng, CHEN Zhipeng, TU Wei

2021, 50(9):  1147-1158.  doi:10.11947/j.AGCS.2021.20210172

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The dynamic precision engineering survey is not only used in the construction but also in monitoring the dynamic change during usage. With the rapid popularization of various sensors and automated mobile platform, such as intelligent vehicles, unmanned aerial vehicle (UAV), unmanned ship, robots etc., the engineering survey is upgraded in an automated, dynamic and intelligent way. It is now armed with the ability to survey in movement. Surveying devices, including mature surveying robots, mobile surveying vehicle, etc., have been proposed. Another way is to assemble devices delicately for specific applications, which can achieve dynamic and precise measurements of position, posture, internal deform and external shape. Focusing on the intelligent device with integrated sensors and specific applications, this paper puts forward the integration principle of multiple sensors and the advanced data processing methods for intelligent engineering surveying. This paper also demonstrates several applications of these devices, such as internal deformation monitoring of the rockfill dam, damage monitoring of the urban underground water pipelines, surveying of the road damages and the damage detection on the south-to-north water diversion project (SNWDP), to show different requirements and solutions in different applications.

Surveying robot and its key technology

YAN Li, CHEN Yu, XIE Hong, DAI Jicheng, ZHAO Yinghao, HU Xiao, LI Yao, ZHAO Leyang, WANG Yueqin

2021, 50(9):  1159-1169.  doi:10.11947/j.AGCS.2021.20210090

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This paper proposes the concept of surveying robot, focuses on analyzing the framework and key technologies of the surveying robot, covers the cutting-edge research fields of surveying robots such as positioning and mapping, scene perception, path planning, and multi-machine collaboration. This paper also summarizes the latest research progress, analyzes the current technical problems and describes the development opportunities of surveying robot.

Some thoughts on deep learning enabling cartography

AI Tinghua

2021, 50(9):  1170-1182.  doi:10.11947/j.AGCS.2021.20210091

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The cartography discipline includes issues of map making and map applications. Both tasks have deep associations with artificial intelligence. Among different intelligence representation methods, the symbolism intelligence approach used to apply with cartography generating mapping expert system technology, the activism intelligence applied with map analysis resulting in optimization decision technology. Nowadays the combination of cartography and connectionism intelligence deep learning faces challenging problems to improve the intelligence level. This study focuses on the issue “deep learning+cartography” discussing three questions. First from the perspective of the consistent ideas in deep learning and map space settlement argues the combination is possible, because both methods have the similar ideas of gradient descent, local spatial association, dimension reduction and non-linear processing. Secondly, by analyzing the mapping characteristics and technology contexts discusses the challenges from the combination, including the irregular data structure in map organization, sample establishment requiring geo-domain knowledge, the integration of geometric and geographic properties and the spatial scale issues in cartography. Thirdly, from the viewpoints of map making and map application respectively examines the practical methods to combine deep learning and cartography.

Research progress and application of spatiotemporal knowledge center

LIU Wanzeng, CHEN Jun, ZHAI Xi, LI Ran, WANG Xinpeng, ZHAO Yong, ZHU Xiuli, XU Zhu, ZHAO Tingting, PENG Yunlu, SHEN Li

2021, 50(9):  1183-1193.  doi:10.11947/j.AGCS.2021.20210160

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Spatiotemporal knowledge is the knowledge with temporal and spatial characteristics formed by summarizing and condensing the information of spatial location, shape, distribution, relationship, movement, change and trend of entity.Spatiotemporal knowledge center is a service platform or environment for acquiring, accumulating, creating, evolving and utilizing spatiotemporal knowledge.The construction of space-time knowledge center is an opportunity and challenge for intelligent mapping. The relevant research is still in the initial stage of exploration, and there is no mature construction and service mode to be used for reference.This article aims at the new needs of natural resource management for surveying and mapping services, draws on the practice of knowledge center construction at home and abroad, incorporates the relevant concepts of intelligent surveying and mapping, and proposes the concept, connotation, technical framework, subject realization methods and research progress of the spatiotemporal knowledge center, and finally introduces the future development direction of spatiotemporal knowledge center from three aspects of development trend, key technology and engineering application.

From geographic information service to geographic knowledge service: research issues and development roadmap

SHEN Li, XU Zhu, LI Zhilin, LIU Wanzeng, CUI Bingliang

2021, 50(9):  1194-1202.  doi:10.11947/j.AGCS.2021.20210183

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Geographic knowledge service (GKS) is widely believed to be the intelligent successor to the current geographic information service (GIS). The need for GKS is becoming ever pressing due to the increasing severity of the information explosion problem. This paper clarifies the confusions in the connotation of GKS, its relationship with GIS, and its requirements for formal representation and intelligent processing of geographic knowledge. After an in-depth analysis of the latest breakthroughs made in artificial intelligence (AI), it is argued that the state-of-the-art AI provides a promising basis of cognitive intelligence for advancing GKS development. Emphasizing the spatio-temporal characteristic of geographic knowledge, this paper identifies three main categories of research issues in GKS development, i.e. those of the knowledge engineering approach to geographic modeling, the cognitively intelligent approach to geographic analysis and the context-aware computing approach to service provision. A graded strategy for advancing GKS is then suggested and a roadmap of GKS development is envisioned.

Multi-agent cooperative control for traffic signal on geographic road network

ZHENG Ye, GUO Renzhong, MA Ding, ZHAO Zhigang, LI Xiaoming

2021, 50(9):  1203-1210.  doi:10.11947/j.AGCS.2021.20210191

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Urban traffic efficiency is one of the key factors affecting urban productivity and is also a crucial topic in the process of smart city construction. With the development of computer technology, artificial intelligence, especially reinforcement learning, plays an increasingly important role in traffic signal control. Currently, traffic signal control based on reinforcement learning is mainly used for the optimization for simple scenarios, such as single road intersection or urban arterial road, not yet for regional coordinated control on an urban geographic road network. This paper is motivated to fill this gap by proposing a two-layered agent cooperative control approach based on reinforcement learning. The first layer implements a coarse-tuning training at a single intersection, where the agents make the single intersection non-blocking by observing the queue length for each lane; In the second layer, the coarse-tuning-trained agent models are put into the geographic network to execute the cooperative fine-tuning training at multiple intersections. This paper conducts the optimization-orientated traffic coordination through a case study of a middle school area in Ningbo. The results show that our control approach is superior to the traditional fixed timing scheme in terms of the passage efficiency.

Geodesy and Navigation

Orbit determination and time synchronization of spatial information network combining sparse regional ground stations

CHEN Ruizhi, YU Baoguo, WANG Fuhong, GONG Xuewen, BAO Yachuan, WANG Lei, LIU Wanke, FU Wenju

2021, 50(9):  1211-1221.  doi:10.11947/j.AGCS.2021.20210009

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The development of global low earth orbit (LEO) satellite communication constellations and the technology advancement of global satellite navigation system (GNSS) orbit and clock determination lay a necessary foundation for moving the continuous operation reference system (CORS) to near-Earth space, which is helpful to break through the limitation of ground reference station distribution and realize global space-based CORS positioning service with few regional ground stations. However,space information network has many different characteristics such as high dynamics, network reconstruction, and significant flexibility. How to determine the orbit of LEO satellites and time datum for a space information network with few regional ground stations is the key to realize global high-precision positioning service of the space-based CORS network. This paper studied the method of unifying space and time datum determination based on the backbone network and the access network strategy. Experiments show that an orbit determination accuracy of about 7 cm is achievable for GNSS and LEO satellites with only five ground stations in China,one in Arctic, one in Antarctica, and 12 LEO satellites. Based on the known precise orbit of space nodes, about 10 cm accuracy is realized for other LEO space nodes that did not participate in orbit determination. For the time synchronization of space information network, the inter-satellite and satellite-ground time comparison method is proposed with the strategy of network layering autonomy and new inter-satellite link. Results show that the time synchronization of 10 ns precision level can be achieved with communication signal system.

Estimation of 3D coseismic deformation with InSAR: an improved SM-VCE method by window optimization

LIU Jihong, HU Jun, LI Zhiwei, ZHU Jianjun

2021, 50(9):  1222-1239.  doi:10.11947/j.AGCS.2021.20200610

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The 3D coseismic deformations derived from the interferometric synthetic aperture radar (InSAR) technique is of great significance for interpreting the characteristic of coseismic movement and inversing the fault slip. Recently, a method for estimating 3D surface deformations with InSAR based on the strain model and variance component estimation (SM, VCE, SM-VCE) is proposed, in which a lot of observation functions are established within a fixed-size window based on the SM, making it possible that the VCE is used to determine the weighting factor of different kinds of InSAR measurements. Compared with the pixel-by-pixel weighted least squares method, this window-based SM-VCE method can obtain a more reliable 3D deformation field. In the original SM-VCE method, the window size is designed to be constant for all points and the observations within the window are considered to be equal-accuracy. These rules are simple, but is easy to be violated for estimating the 3D coseismic deformation. Particularly, the case is easy to occur that the InSAR technique cannot obtain valid measurements in the near fault region, resulting in fact that the original SM-VCE method with a fixed-size window would fail to derive the near-field 3D coseismic deformation. Even if the near-field observations are available, the window-based SM-VCE method cannot estimate accurate 3D deformation due to the incorporation of inhomogeneous points across the fault. Besides, the accuracy of InSAR measurements within the window is generally various due to the decorrelation noise, which is not considered in the original SM-VCE method. In this paper, the surrounding points are selected based on a window with adaptive size as well as the fault lines so that the 3D deformation in the near fault zone can be obtained. Furthermore, an iterative weighted least squares method is employed to determine the relative weight of InSAR measurements within the window before the implementation of VCE. Simulation and real experiments of the 2019 Mw7.1 Ridgecrest earthquake are conducted with the Sentinel-1 data, demonstrating that the improved SM-VCE method by window optimization in this paper can obtain a more accurate and complete 3D deformation field compared with the original SM-VCE method.

Photogrammetry and Remote Sensing

Sliding window Gaussian fitting algorithm for ranging error suppression of full-waveform spaceborne laser

XIE Junfeng, LIU Ren

2021, 50(9):  1240-1250.  doi:10.11947/j.AGCS.2021.20200466

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The ranging accuracy of spaceborne laser altimeter is one of the main sources that affect the geometric calibration and processing accuracy of the laser.Aiming at the issues of laser ranging extraction lower accuracy and the stability, caused by the quantization error of the full-waveform spaceborne laser analog signal after digital processing, so a sliding window Gaussian fitting algorithm for ranging error suppression of full-waveform spaceborne laser is proposed.This method uses a sliding window to eliminate noise points near the peak, and optimizes the peak value of the waveform to extract a more accurate ranging value.Finally, the GF-7 laser altimeter was used as the test object, the relative and absolute accuracy of laser elevation were compared and verified by using the ice surface, inland lake and the flat ground.The results indicate that, the laser ranging accuracy of the paper algorithm is improved by 7.5 cm compared with the general peak method. Based on the ranging value of the paper method, the relative accuracy of laser elevation is improved by 4.3 cm. The absolute accuracy of laser elevation is increased by 4.5 cm verified by airborne LiDAR point cloud data. Which fully shows that the method in this paper can be used as a method to reduce effectively the random error of laser ranging, and provides an indispensable foundation for the sub-meter elevation measurement accuracy of the GF-7 satellite.

Automatic classification and vectorization of road markings from mobile laser point clouds

FANG Lina, WANG Shuang, ZHAO Zhiyuan, FU Huasheng, CHEN Chongcheng

2021, 50(9):  1251-1265.  doi:10.11947/j.AGCS.2021.20200351

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Road markings are important traffic safety facilities. Its location, attribute, and topological relationship finely describe road traffic structure, and it is the basic data for applications such as intelligent traffic, high-precision maps, location, and navigation. This paper proposes a graph attention network with spatial context information (GAT_SCNet) to classify the road markings from mobile LiDAR point clouds. GAT_SCNet explores the graph structure to establish the appearance and dependence information among road markings. Meanwhile, GAT_SCNet incorporates the multi-head attention mechanism into the node propagation step, which computes the hidden states of each node based on the geometric, topological, and spatial structure relationships of the neighboring nodes. Finally, road markings classification is realized by the classification of nodes. Then, some schemes are designed for road markings vectorization. Four test datasets consisting of urban and highway scenes by different mobile laser scanning systems are used to evaluate the validities of the proposed method. Four accuracy evaluation metrics precision and recall of 9 types of road markings on the selected test datasets achieve (100.00%, 93.77%, 100.00%, 100.00%, 100.00%, 96.73%, 97.96%, 100.00%, 98.39%) and (100.00%, 96.36%, 100.00%, 10.000%, 100.00%, 97.26%, 85.72%, 100.00%, 94.16%), respectively. Accuracy evaluations and comparative studies prove that the proposed method has the capability of classifying multi-type road markings simultaneously and distinguishing similar road markings like dashed markings, zebra crossings, and stop lines in complex urban scenes.

Cartography and Geoinformation

Modeling and analysis of urban housing price models based on multiscale geographically and temporally weighted regression

YE Jian, HU Xin, XU Hongmeng, CHEN Xi, Lü Qi

2021, 50(9):  1266-1274.  doi:10.11947/j.AGCS.2021.20210010

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Scale, time, and spatial distance have always been the key to restricting the accuracy of geographically and temporally weighted regression (GTWR) models. Based on the Euclidean distance and road network distance constraints, this research extends the spatio-temporal multiscale to the modeling of geographically and temporally weighted regression methods to test the improved performance of the multiscale GTWR model and to verify the superiority of the road network distance constraint in the multiscale GTWR model. Herein, the commercial housing communities during the time period of 2015—2018 in the main urban area of Chengdu are considered as the case object, and the multiscale GTWR and GTWR are compared in terms of goodness of fit, residual sum of squares (RSS), and Akaike information criterion (AIC). Experimental results show that compared with GTWR, the multiscale GTWR provides a more effective explanation for the independent variables affecting housing prices, and improves the rationality of the model by using the road network distance.The goodness of fit based on the Euclidean distance and road network distance constraints has been improved by 0.123 and 0.208, respectively, and the RSS and AIC have been effectively reduced. Compared with the GTWR and multiscale GTWR models based on the Euclidean distance constraint, the goodness of fit of both GTWR and multiscale GTWR models based on the road network distance constraint has been improved by 0.007 and 0.092, respectively. Furthermore, the calculation results based on the road network distance confirm the correctness of the multiscale GTWR model and prove that the multiscale GTWR model exhibiting comprehensive consideration of scale and spatio-temporal distance has good versatility. Results obtained herein are expected to provide an important reference for urban planning in spatial-temporal multiscale modeling.

Summary of PhD Thesis

Research and applications on methodologies for three-dimensional geological and roadway modeling in coal mine

JIA Qingren

2021, 50(9):  1275-1275.  doi:10.11947/j.AGCS.2021.20200370

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Study on spectral analysis method and remote sensing application of coal and coal gangue

SONG Liang

2021, 50(9):  1276-1276.  doi:10.11947/j.AGCS.2021.20200372

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Study on ionosphere anomaly monitoring based on multi-source data and its effect on GNSS

CHENG Na

2021, 50(9):  1277-1277.  doi:10.11947/j.AGCS.2021.20200382

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Research on several issues of oblique stereo image matching

YU Mei

2021, 50(9):  1278-1278.  doi:10.11947/j.AGCS.2021.20200401

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