Loading...

Table of Content

    20 August 2021, Volume 50 Issue 8
    Smart Surveying and Mapping
    Smart surveying and mapping: fundamental issues and research agenda
    CHEN Jun, LIU Wanzeng, WU Hao, LI Songnian, YAN Li
    2021, 50(8):  995-1005.  doi:10.11947/j.AGCS.2021.20210235
    Asbtract ( )   HTML ( )   PDF (1177KB) ( )  
    References | Related Articles | Metrics
    Surveying and mapping production and services are facing many new problems such as real-time data acquisition, automation of information processing, and intellectualization of service applications. The main reason is that digital surveying and mapping involves complex algorithms and models as the core, and it is often impossible to use simple algorithms and models to completely describe and express the diverse, multi-dimensional and dynamic real world, which makes it difficult to address high-dimensional, nonlinear spatial problems. In order to address this challenge, it is necessary to explore the use of natural intelligence in surveying and mapping, and develop smart surveying and mapping technologies guided by knowledge and based on algorithms. This paper first discusses the basic concepts and ideas of smart surveying and mapping and then analyzes and points out the three basic problems of smart surveying and mapping, including the analysis and modeling of natural intelligence in surveying and mapping, the construction and realization of hybrid intelligent computing paradigm, and the mechanism and path of empowering production. The main directions for further development in the near future are then proposed, including the construction of knowledge system of smart surveying and mapping, research of technology methods, research and development of application systems and instrument equipment. In order to effectively promote scientific research and engineering applications in this area, efforts should be placed on top-level designing, promoting interdisciplinary collaborative innovation and industry-university-research cooperation, and creating good development environments.
    PNT intelligent services
    YANG Yuanxi, YANG Cheng, REN Xia
    2021, 50(8):  1006-1012.  doi:10.11947/j.AGCS.2021.20210051
    Asbtract ( )   HTML ( )   PDF (980KB) ( )  
    References | Related Articles | Metrics
    The important research direction of positioning, navigation and timing (PNT) is PNT intelligent services or smart PNT services. The most significant function of the PNT intelligent services is to sense the environment and the user requirements, to realize the intelligent integration of PNT information, intelligent modification of observation model and the intelligent fusion of multi-PNT information, and then to deliver the PNT service accordingly. This paper describes and analyzes the key techniques of intelligent PNT in different aspects, including the intelligent sensing, intelligent model, intelligent data integration and intelligent service. Additionally, the criteria for the PNT intelligent services are proposed, that is, the "availability criterion" for the intelligent PNT information integration, the "reliability criterion" for the intelligent functional model optimization, the "uncertainty criterion" for the stochastic model intelligent modification, the "accuracy criterion" for the intelligent PNT data fusion, and the "efficiency criterion" for the smart PNT services, as well as the "continuity criterion" for high dynamic users. The study further indicates that the comprehensive (or integrated) PNT is the foundation of the resilient PNT, the resilient PNT is the foundation of the intelligent PNT, and the intelligent PNT is the key development direction of PNT services.
    Status analysis and research of sample database for intelligent interpretation of remote sensing image
    GONG Jianya, XU Yue, HU Xiangyun, JIANG Liangcun, ZHANG Mi
    2021, 50(8):  1013-1022.  doi:10.11947/j.AGCS.2021.20210085
    Asbtract ( )   HTML ( )   PDF (1747KB) ( )  
    References | Related Articles | Metrics
    The rapid development of earth observation projects in China has obtained a large volume of multi-source (multi-type sensors, multi-temporal, multi-scale) remote sensing data. But the capability of intelligent remote sensing image processing lags behind data acquisition. In recent years, people have significantly improved the effectiveness of image feature extraction with deep learning networks. But limited number and variety of sample data is not enough for processing the multi-source remote sensing images. This paper analyzed existing sample datasets and proposed a method for constructing a sample database for intelligent remote sensing image interpretation, including the data model, classification system, data organization, as well as the Internet-based platform for collaborative sample collection and sharing.
    Study on man-machine collaborative intelligent extraction for natural resource features
    ZHANG Jixian, LI Haitao, GU Haiyan, ZHANG He, YANG Yi, TAN Xiangrui, LI Miao, SHEN Jing
    2021, 50(8):  1023-1032.  doi:10.11947/j.AGCS.2021.20210102
    Asbtract ( )   HTML ( )   PDF (1414KB) ( )  
    References | Related Articles | Metrics
    Carrying out an integrated survey, monitoring and evaluation of natural resources, accurately understanding the status and changes of various natural resources in China, is the scientific basis for territorial and spatial plans, and gradually realizing the overall protection, restoration, and comprehensive management of landscapes (including mountains, forests, fields, lakes and grasses), ensuring national ecological security. At present, the extraction of natural resource features based on remote sensing images mainly relies on visual interpretation via man-machine interaction and field spot verification. It needs high labor intensity, and production efficiency is low. The results are also highly affected by man-induced factor, which can no longer adapt to the requirements for integrated investigation and monitoring of all features of natural resources. This paper conforms to the emerging direction of the research development with artificial intelligence collaboration. Firstly, this paper reviews the main research progress of deep learning technology and its application in the field of remote sensing image intelligent extraction systematically, and analyzes its limitations, then it reviews the main research status of man-machine collaboration. Afterward, Starting from the characteristics of natural resource features, presents a technical framework of "intelligent background computing+intelligent engine+man-machine interface" for man-machine collaborative intelligent extraction. The key technologies that need to be overcome are described. At last, the idea of creating cloud platform for feature extraction are discussed. This paper aims to provide a new AI method for intelligent extraction and improve the automation and intelligence level of natural resource feature extraction.
    Cartographic representation of spatio-temporal data: fundamental issues and research progress
    LI Zhilin, LIU Wanzeng, XU Zhu, TI Peng, GAO Peichao, YAN Chaode, LIN Yan, LI Ran, LU Chenni
    2021, 50(8):  1033-1048.  doi:10.11947/j.AGCS.2021.20210072
    Asbtract ( )   HTML ( )   PDF (13216KB) ( )  
    References | Related Articles | Metrics
    Big spatio-temporal data bring about unprecedented challenges to cartographic representation. Through analysis of the main characteristics of spatio-temporal data and consequent requirements on cartographic presentation, we believe that the fundamental issues on the cartographic representation of spatio-temporal data include the mathematicization of fundamental theories, quantitative optimization of cartographic design, on-demand adaptive representation methodology, modeled quality prediction, and ubiquitous mapping applications. Then, a brief review of these issues are carried out under headings:progress in mathematicization of fundamental theories, progress in methodological development, and progress in ubiquitous mapping applications. Finally, an outlook is also presented.
    Artificial intelligence for reliable object recognition from remotely sensed data: overall framework design, review and prospect
    SHI Wenzhong, ZHANG Min
    2021, 50(8):  1049-1058.  doi:10.11947/j.AGCS.2021.20210095
    Asbtract ( )   HTML ( )   PDF (1229KB) ( )  
    References | Related Articles | Metrics
    Reliability is one of the important features in remotely sensed data-based land use monitoring. Artificial intelligence (AI) technology promotes the rapid development of object recognition from remotely sensed data. However, the un-explainability in such image processing causes reliability problems. Based on the reliability theory and the basic theory of AI, this paper first presents the idea and the overall framework of intelligent and reliable object recognition. Second, the core research directions, including analysis of influencing factors, improvement methods, evaluation methods, and process control for reliability are sequentially introduced. Finally, the future development trend of AI for reliable object recognition from remotely sensed data is outlined.
    A deep learning network for semantic labeling of large-scale urban point clouds
    YANG Bisheng, HAN Xu, DONG Zhen
    2021, 50(8):  1059-1067.  doi:10.11947/j.AGCS.2021.20210093
    Asbtract ( )   HTML ( )   PDF (12012KB) ( )  
    References | Related Articles | Metrics
    In recent years, point cloud has become an important type of 3D spatial data. How to improve the understanding abilities of point cloud using artificial intelligence for correct semantic labeling and accurate detection of objects is an urgent and difficult problem. This paper hence proposes an end-to-end 3D point cloud deep learning network, which effectively guarantees the efficiencies of point cloud sampling, the accuracy of feature extraction and the optimization of the overall network performance by the up-down sampling strategy of irregular distribution point cloud, multi-layer aggregation and propagation of features and the loss function for uneven samples. The studies on the large-scale 3D point cloud benchmark data show that it achieves excellent performance in semantic labeling for large-scale outdoor scenes of point clouds, better than those of the state-of-art deep learning networks of point cloud, providing a strong support for the high-performance extraction of 3D geospatial information.
    Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images
    ZHANG Yongjun, WAN Yi, SHI Wenzhong, ZHANG Zuxun, LI Yansheng, JI Shunping, GUO Haoyu, LI Li
    2021, 50(8):  1068-1083.  doi:10.11947/j.AGCS.2021.20210079
    Asbtract ( )   HTML ( )   PDF (43935KB) ( )  
    References | Related Articles | Metrics
    The history and recent development of photogrammetry and remote sensing are reviewed and analyzed firstly. Then the novel concept of "photogrammetric remote sensing" is put forward to meet the needs of accurate and fast processing of multi-source remote sensing images in the new era of big data and intelligent surveying and mapping. The new photogrammetric remote sensing discipline is the deep integration of the frontier theories and technologies of photogrammetry and remote sensing, and concentrates on solving the theories and technologies about simultaneously determine the geometric positions, physical attributes, semantic information and temporal changes of interested scenes and objects. Its theoretical and fundamental basis are photogrammetry, remote sensing, artificial intelligence, big data processing, and high-performance computation, etc. It will break through the current isolated status and serial technical route that photogrammetry mainly focuses on geometric processing, while remote sensing mainly focuses on semantic information extraction and inversion. It forms an innovation of the closed-loop fusion of semantic extraction and geometric processing. A novel geometric-semantic integrated processing framework is formed through the deep fusion of geometric model and spectral radiative and reflective information. Based on the proposed concept of photogrammetric remote sensing, this paper discusses the main scientific problems and related research and application fields, and then attempts to build a new theoretical and technical framework of integrated intelligent photogrammetric remote sensing processing of multi-source satellite images. Closed-loop fusion of semantic information extraction and accurate geometric processing has significantly improved the level of accuracy, automation and intelligence. The correctness and effectiveness of the proposed theory and methods are preliminarily verified by several practical applications.
    Intelligent and multi-scale surveying of key areas and processes of the Earth system
    HAO Tong, WANG Xiaofeng, FENG Tiantian, LU Ping, QIAO Gang, XIE Huan, LI Rongxing
    2021, 50(8):  1084-1095.  doi:10.11947/j.AGCS.2021.20210109
    Asbtract ( )   HTML ( )   PDF (6883KB) ( )  
    References | Related Articles | Metrics
    As the most sensitive key area of the Earth system to global climate change, research on the key processes of the cryosphere has attracted much attention. Meanwhile, the inevitable trend in the multi-scale monitoring study of key processes of the Earth system is to move from digital surveying to intelligent surveying and mapping. This article summarizes the research status quo on the key processes of the Earth system in terms of intelligent observation and processing in five aspects:glacier melting and sea level rise, polar ice cap stability and subsurface structure, Arctic and Antarctic sea ice changes and extreme climates, permafrost degradation and geological disasters, and livable urban underground spaces under the Earth system. Furthermore, it provides the outlook of the development trend of the intelligent surveying and mapping of key areas and key processes of the Earth system, i.e., to improve the intelligent comprehensive monitoring network, to establish big data centers for key areas, and to build the intelligent model and prediction system of key processes.
    The direction of integration surveying and mapping geographic information and artificial intelligence 2.0
    ZHANG Guangyun, ZHANG Rongting, DAI Qionghai, CHEN Jun, PAN Yunhe
    2021, 50(8):  1096-1108.  doi:10.11947/j.AGCS.2021.20210200
    Asbtract ( )   HTML ( )   PDF (1162KB) ( )  
    References | Related Articles | Metrics
    In the artificial intelligence (AI) 2.0 era, based on the analysis of the development status of surveying and mapping geographic information, the driving force of promoting surveying and mapping geographic information to the era of intelligence 2.0 is studied, and the key tasks of building the surveying and mapping geographic information 2.0 era are proposed. In terms of basic theories, it is proposed that the breakthroughs of theories should be made in the twin environment of spatiotemporal big data, the knowledge graph of spatiotemporal information, the true three-dimensional deep neural network, the dynamic prediction and reasoning of spatiotemporal big data, etc. In terms of key technologies, the methods are emphasized, integrating the five intelligent technologies containing big data intelligence, cross-media intelligence, crowd intelligence, hybrid-augmented intelligence and autonomous unmanned system with surveying and mapping geographic information. In terms of platform, the important role and construction method of software and hardware platform of intelligent surveying and mapping geographic information are emphasized. At the end, taking the intelligent monitoring of natural resources and indoor-outdoor integrated intelligent navigation as a typical case, the practical application of related theories, technologies and platforms in surveying and mapping geographic information industry is analyzed. Through the discussion of this paper, the connotation, key content and path of the surveying and mapping geographic information enabled by AI are clarified, providing a kind of thinking for surveying and mapping geographic information industry to move towards the new era of intelligence 2.0.
    Pattern and directions of spaceborne-airborne-ground collaborated intelligent monitoring on the geo-hazards developing environment and disasters in glacial basin
    WU Lixin, LI Jia, MIAO Zelang, WANG Wei, CHEN Biyan, LI Zhiwei, DAI Wujiao, XU Wenbin
    2021, 50(8):  1109-1121.  doi:10.11947/j.AGCS.2021.20210107
    Asbtract ( )   HTML ( )   PDF (3660KB) ( )  
    References | Related Articles | Metrics
    As the climate in western China becomes increasingly wet and warm, the geo-hazards developing environment in glacial basin varies quickly and the glacial disasters occur frequently. The glacial basin is characterized by rough topography, remote places, and fast changing land covers. More importantly, the glacial disasters are usually developed in chain and the mass source is located in very high altitude. Consequently, the conventional spaceborne-airborne-ground collaborated monitoring technologies on landslide and open-pit slope slide cannot play an effective role on monitoring the variation of glacial basin and glacial disasters. In this paper, based on the contents and difficulties of monitoring, we introduce the usable modern surveying and remote sensing technologies, and analyze their limits. Subsequently, aiming at the different monitoring elements and quality requirements, we propose the connotation of spaceborne-airborne-ground collaborated intelligent monitoring on the glacial basin and glacial disasters, i.e., platform collaboration, time collaboration, parameter collaboration and scale collaboration, and design a task-driven and knowledge-guided technical pattern. Finally, targeting on three fundamental tasks, i.e., the simulation of disaster scenario in single glacier basin, the study of geo-hazards developing environment and risk recognition in multiple glacier basins, and the emergency response of glacial disaster, we present the preliminary schemes of spaceborne-airborne-ground collaborated intelligent monitoring. Finally, the intelligent issues of the spaceborne-airborne-ground collaborated monitoring on the geo-hazards developing environment and disasters in glacial basin are discussed, and the future works are planned.
    Remote sensing image intelligent interpretation: from supervised learning to self-supervised learning
    TAO Chao, YIN Ziwei, ZHU Qing, LI Haifeng
    2021, 50(8):  1122-1134.  doi:10.11947/j.AGCS.2021.20210089
    Asbtract ( )   HTML ( )   PDF (2740KB) ( )  
    References | Related Articles | Metrics
    Accurate interpretation of remote sensing image (RSI) plays a vital role in the implementation of remote sensing applications. In recent years, deep supervised learning has achieved great success in the field of RSI interpretation by its soaring performance on representation learning. However, this method heavily relies on large-scale and high-quality labeled data, while building a big remote sensing data set is extremely expensive because of the unique spatial-temporal heterogeneity of remote sensing data. This contradiction seriously restricts the performance of deep supervised learning in large areas and complicated remote sensing scenes. How to solve the last mile problem in the field of RSI accurate interpretation becomes urgent. This paper first systematically reviews the main research progress of supervised learning methods in the field of RSI interpretation, and then analyzes its limitations. Afterward, we introduce the concept of self-supervised learning and detail how it works for unsupervised feature learning. Finally, we briefly discuss open problems and future directions of self-supervised learning if it is applied in the field of RSI interpretation, with the aim of providing a new perspective for RSI interpretation with the adoption of huge unlabeled data.
    Summary of PhD Thesis
    Research on semantic location network based on address database and spatial positioning method
    WANG Wei
    2021, 50(8):  1135-1135.  doi:10.11947/j.AGCS.2021.20200007
    Asbtract ( )   HTML ( )   PDF (710KB) ( )  
    Related Articles | Metrics
    Research on the change information extraction of urban road network and its impact on land use
    WANG Shuai
    2021, 50(8):  1136-1136.  doi:10.11947/j.AGCS.2021.20200412
    Asbtract ( )   HTML ( )   PDF (710KB) ( )  
    Related Articles | Metrics