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This paper takes smart cartography in the context of artificial intelligence as the starting point, analyzes the impact of space-time big data on cartography, points to three possible directions for the evolu‐ tion of the map, explains the connotation of smart cartography and its characteristics of crowd-sourced da‐ ta, user participation, model coupling and visual analysis, discusses in depth the explicit and implicit knowl‐ edge representation of smart cartography, proposes the idea of establishing a map knowledge graph, and outlines a platform development paradigm based on low-code technology. [ABSTRACT FROM AUTHOR]

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]

Objectives: The spatial delimitation of urban area is the foundation for monitoring the urbanization level and determining city scale from the perspective of geographical space.Methods: Based on the requirements of urban area defined in Document No. 51 of the State Council, this paper clarifies the connotations of urban area with further understandings and proposes a process for delimiting urban area in geographical space by connecting map polygons of governmental residents and neighborhood committee, which can reduce the subjectivity and fuzziness in the spatialization of process of urban area. Results: This paper develops the main idea and spatial delimitation about urban area and takes Wuhan as an example to illustrate the details about spatial delimitation of urban areas.Conclusions: This paper mainly discusses how to unify urban delineation under the current technical conditions, and does not mention how to use other auxiliary data, such as the application of multi⁃source big data in urban delineation, because of the unbalanced development of big data infrastructure in different cities. But it does not mean that the application of population big data and urban big data is not important.On the contrary, the application of big data in the work of urban area⁃ delimitation is the next research focus and the future development trend. [ABSTRACT FROM AUTHOR]

The optimal allocation of land‐use is an important technical means to solve the contradiction be‐ tween supply and demand of land resources and to coordinate social and economic development with eco‐ logical protection, and it is also one of the key issues that need to be solved for the sustainable use of land resources. With the development of spatial simulation and intelligent optimization technology, the theoretical basis and methodological system of land‐use optimization are constantly updated. Based on bibliometrics analysis, this paper systematically summarized the driving theories and methodology of land‐use optimal al‐ location at home and abroad, focused on the application of spatial simulation models and intelligent optimization models in land‐use optimization problems, discussed and prospected the challenges and the future development of optimal land‐use allocation, and put forward that: The coupling drive of natural and human processes, spatial collaboration of multiple models, embedding big data knowledge mining methods, and the support of digital twin technology, should be the research direction of the next stage in the field of optimal land‐use allocation. [ABSTRACT FROM AUTHOR]