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Firstly, this paper discusses the change in the way of thinking and working of the cartographers and cartographic engineering through data, big data and space-time big data, which promotes cartography to enter a new era of the “fourth scientific paradigm” characterized by “data-intensive computing”. Secondly, this paper analyzes the functions and features of the digital transformation results of 4D products, and the progress in diversification and individuation of the map products, and the “design - compilation-publication” integrated digital transformation of mapping, and the feature of the representative atlases published in 2021. Finally, by giving two specific examples, i.e., the intellectualization of map generalization and digital mapping, this paper reviews four evolutionary stages of map generalization in the era of artificial intelligence and summarizes the difficulties and breakthroughs in key technologies, and provides a profound conclusion of the research status and the issues to be further explored in the domain. [ABSTRACT FROM AUTHOR]

Geographic Information System has made great progress in theory and technology in recent 60 years. The application field of GIS has expanded to all aspects of society, and the social influence is increas‐ able growing. The architecture, development mode and service mode of GIS have undergone profound changes. In order to promote the further development of GISystem, this paper focused on the following three issues based on summarizing the three meanings of “S” in “GIS”, namely “System” “Science” and “Service”, and the fruitful achievements of “GIS” in recent 60 years. The first issue is how to un‐ derstand GISystem. This issue should be analyzed from two aspects, and one is to discuss the connotation of geographic information system through analyzing the three key words (namely system, information and geographic) of GIS. The other one is to analyze the relationship between GIS and map, computer mapping and map database. This paper holds the idea that GIS originates from and goes beyond map, computer map‐ ping and map database. It should be considered that GIS has the characteristics of equipment. The second issue is how does GIS evolve. This issue focuses on the social demand background, technical background and discipline background of the development and evolution from “Geographic Information System” to “Geographic Information Service”. It also focuses on the main manifestations of the development and evo‐ lution of GIS from the aspects of application field, expansion of data resources and functions, architecture, development mode and service mode. The third issue is where will the future development of GIS go. We discussed that the limit application domain expansion of GIS and point out that the future development of GIS must face the urgent needs of national economic construction and national defense construction. Based on the analysis and comparison of three existing GISystem service modes, we consider that the “hybrid” technology system of “grid integration” and “elastic cloud” is the best choice for GISystem service mode. Finally, this paper puts forward six key technical problems that must be solved in the implementation of “hybrid” spatiotemporal big data platform technology based on “grid integration” and “elastic cloud”and designs the application mode of “spatiotemporal big data platform”. [ABSTRACT FROM AUTHOR]

At present, the development of the geographic information industry is facing severe challenges of transformation and upgrading and great development opportunities. The rapid development of emerging information technologies such as "Internet + ”, big data, cloud computing, and artificial intelligence has provided strong support for the transformation and upgrading of the geographic information industry. In this paper, a series of issues must be seriously considered, such as the background. goal. ways, driving forces of transformation and upgrading. For the background of the transformation and upgrading of the geographic information industry, this paper proposes the space-time view of big data and the theory of space-time big data based on the understanding of the geographic information industry and big data. For the goal of the transformation and upgrading in geographic information industry, the theory of space-time big data platform and the model of "General Space-Time Big Data Platform+” are proposed, which will become a transformation and upgrading version of the geographic information industry. For the ways of the transformation and upgrading of the geographic information industry, the technical system and technical methods for constructing the space-time big data platform are proposed. Beginning with the basic research, the way of the industrialization of space-lime big data is discussed, which constructs a space-time big data product system consisting of hardware prod ucls. software products, software and hardware integration products and various types of data prod ucts. For the driving force of the transformation and upgrading of the geographic information industry. artificial intelligence technology will become the core driving force for the transformation and upgrading of the geographic information industry. The role of algorithms, data and computing power of artificial intelligence in the industrialization of space-time big data and the relationship between the three are also discussed. [ABSTRACT FROM AUTHOR]

City is the place aggregated massive human activities. City is the exchange hub of population flow, goods flow, information flow and currency flow, which is highly dynamic and complex. Smart city provides various tools to acquire spatiotemporal big data, such as satellite and drone remote sensing, mobile sensing, social sensing, crowdsourcing sensing, etc., which enable us to sense urban dynamics. This paper introduces the framework of urban dynamic sensing, describes the typical applications of spatial dynamics, human behavior dynamics and space behavior interaction dynamics, and discusses the problems, such as the uncertainty in spatiotemporal big data, the multi ⁃view ensemble learning in urban sensing, the verification of the urban dynamic results and the cascading influence of multi⁃urban factors. Outlooking the future, the study of urban dynamics should combine with real⁃time Internet of things data to sense multi⁃dimensional, multi⁃spatiotemporal resolution urban dynamic to enable refined urban governance and to effectively solve urban problems. [ABSTRACT FROM AUTHOR]

Speed is key to prevent and control major public health emergencies. For an efficient management, time, data, information, and resources are important factors. This paper summarizes the challenges in China’s response to the coronavirus disease 2019 （COVID-19）， and proposes an integration of vertical system and horizontal system, based on the idea of“let data run more, rely on accurate information, and outperform viruses with electromagnetic waves ”. Through all the analyses, we provide suggestions on improving China’s digital public health emergency management system for scientific and technological strategies, and they include: (1) establishing a standardized data epidemic information collection system to dynamically sense public sentiment; (2) building a public health big data center with a unified spatiotemporal benchmark to achieve cross regional and cross industry data aggregation, fusion, and sharing; (3) strengthening artificial intelligence technology for epidemic monitoring, forecasting, and risk research, and improving management accuracy and screening efficiency; (4) developing situation deduction and optimizing regulatory computing technology and services scientific dispatch and emergency command; (5) improving operating system mechanisms to ensure normal, safe and stable operations. [ABSTRACT FROM AUTHOR]

With the construction of the smart city, the maturing sensor technology makes the spatio-temporal available. Every year, the amount of various kinds of spatio-temporal data from remote sensors, map servers, video monitoring, crawler and social platform reach up to PBs. Human society has entered the era of spatio-temporal big data. However, the sharp contrast between the sudden increase of mass data and the lack of information appreciation mechanism appears. The geo-knowledge produced by only IT are blacked seriously. How to use the emerging technology, combined with geographic information and accordingly analysis means, to produce, mine and use the spatio-temporal data has become the key problem to be solved urgently. Geo-knowledge is centered on human spatial cognition and complex knowledge discovery technology, which is of great significance for intelligent applications and government decision-making in the age of big data. In the era background of spatio-temporal big data, this paper discusses the connotation, time features, structure and research directions of geo-knowledge engineering in the future. Strongly driven by the virtual geographical environment, digital earth, smart city, geo-know-ledge engineering will promote the traditional GIS transforming to the directions of human cognition and exploration procedure. Through the rapid transformation from massive spatio-temporal data to the geo-knowledge, geo-knowledge engineering promises to solve the challenge problem of "geo-know-ledge explosion but knowledge poor" which will increase the efficiency of data use. [ABSTRACT FROM AUTHOR]

Artificial intelligence(AI) will affect various fields and professions. Geoinformatics and remote sensing are closed the field of artificial intelligence. Our discipline will have a good development chance, also face a big challenge. This paper firstly introduces the domain of AI and the fields related geoinformatics and remote sensing, then presents the progresses of photogrammetry and remote sen-sing applications based on computing vision and machine learning. Finally, some research progresses involved perceive and reasoning based on space-time big data have revealed the application prospect in sensing, perceive and reasoning for the nature and society based on space-time data from geoinforma-tics and remote sensing. A desire is to push the quick development of geoinformatics and remote sen-sing in AI era. [ABSTRACT FROM AUTHOR]