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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]

With the development of information technology, the rise of surveying and mapping big data and artificial intelligence, the lack of data is no longer a problem. However, the existing surveying and mapping data processing technology has been pursuing the accuracy of data (micro), and big data research just allows the data to be mixed and uncertain (macro). Therefore, although the traditional surveying and mapping data processing theory has accumulated a large number of technical advantages in micro data processing, the large‑scale and complexity of big data has become increasingly prominent, in which traditional calculation model and analysis algorithm cannot effectively support the efficient analysis and processing of big data. As the key to the intelligent era, data processing theory and method, how to adapt to the challenges and opportunities of new technology is worth our deep thinking. Driven by big data, new ideas and methods such as large‑scale data mining, machine learning and deep learning are booming, which greatly promote the fusion of multi‑source heterogeneous big data inside and outside the scene, effectively extract surface feature information from a variety of sensor data, and constantly improve the ability of surveying and mapping information acquisition and analysis. We think that the theory of surveying and mapping data also needs to be followed up, and the existing data processing methods need to be intelligent. Combined with the frontier hot spots, development trends and existing challenges of intelligent surveying and mapping, this paper explores the expansion direction of data processing theory. One is to promote the further development of surveying data processing theory, and the other is to provide reference for graduate students who are interested in entering the field of surveying and mapping big data. [ABSTRACT FROM AUTHOR]

The technological progress in the field of artificial intelligence (AI) has brought new opportunities and challenges to the intelligent development and innovative research in geospatial related fields. Geospatial artificial intelligence (GeoAI) refers to the interdisciplinary research direction that combines geography, earth science and artificial intelligence, and seeks to solve major scientific and engineering problems in human-environmental interaction systems through the research and development of spatial intelligence in machines to improve the dynamic perception, intelligent reasoning and knowledge discovery of geographic phenomena and earth science processes. This paper briefly summarizes the historical origins of GeoAI development, introduces spatially explicit and implicit AI models, reviews recent GeoAI research and applications(including spatial representation learning,spatiotemporal prediction and spatial interpolation,monitoring of geographic resources and environment, cartography, and geo⁃text data semantic analysis), and identifies several potential research challenges and directions for the future development of GeoAI. [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]