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Nowadays Surveying and Mapping (S&M) production and services are facing some serious challenges such as real-timization of data acquisition, automation of information processing, and intellectualization of service applications. The main reason is that current digitalized S&M technologies, which involve complex algorithms and models as the core, are incapable of completely describing and representing the diverse, multi-dimensional and dynamic real world, as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models. In order to address these challenges, it is necessary to explore the use of natural intelligence in S&M, and to develop intelligentized S&M technologies, which are knowledge-guided and algorithm-based. This paper first discusses the basic concepts and ideas of intelligentized S&M, and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M, the construction and realization of hybrid intelligent computing paradigm, and the mechanism and path of empowering production. Further research directions are then proposed in the four areas, including knowledge systems, technologies and methodologies, application systems, and instruments and equipments of intelligentized S&M. Finally, some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

Over the last decade, event prediction has drawn attention from both academic and industry communities, resulting in a substantial volume of scientific papers published in a wide range of journals by scholars from different countries and disciplines. However, thus far, a comprehensive and systematic survey of recent literature has been lacking to quantitatively capture the research progress as well as emerging trends in the event prediction field. Aiming at addressing this gap, we employed CiteSpace software to analyze and visualize data retrieved from the Web of Science (WoS) database, including authors, documents, research institutions, and keywords, based on which the author co-citation network, document co-citation network, collaborative institution network, and keyword co-occurrence network were constructed. Through analyzing the aforementioned networks, we identified areas of active research, influential literature, collaborations at the national level, interdisciplinary patterns, and emerging trends by identifying the central nodes and the nodes with strong citation bursts. It reveals that sensor data has been widely used for predicting weather events and meteorological events (e.g., monitoring sea surface temperature and weather sensor data for predicting El Nino). The real-time and multivariable monitoring features of sensor data enable it to be a reliable source for predicting multiple types of events. Our work offers not only a comprehensive survey of the existing studies but also insights into the development trends within the event prediction field. These findings will assist researchers in conducting further research in this area and draw a large readership among academia and industrial communities who are engaged in event prediction research. [ABSTRACT FROM AUTHOR]

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning. [ABSTRACT FROM AUTHOR]