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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]

Due to the blockage of satellite signals by the buildings and other structures, the high-accuracy, reliability and real-time indoor location-based services is one of the challenges for BeiDou navigation satellite system. As one of the core technologies of the spatio-temporal digital foundation for future artificial intelligence and super-intelligence applications, high-precision indoor positioning has long been a research hotspot in industry and academia. This paper first reviews the development status of existing high-precision indoor positioning technologies, and compares their advantages, disadvantages and difficulties. Then it analyzes the development trend of related technologies under typical applications, and looks forward to the fu ture development direction and application scenarios of indoor positioning technologies. [ABSTRACT FROM AUTHOR]

From the perspective of map cognition, this paper analyzes the important role of mental map in a successful event of family search. Firstly, several basic concepts of map cognition such as spatial cognition, mental map and cognition mapping are clarified, and the position and function of mental map in traditional cartographic triangle are expounded. A detailed empirical analysis is made on the cognitive process of both the seeker and the sought in this case. Three geospatial cognition problems in the interview are discussed: The adaptability difference of map tools to spatial cognition stage, the spatial scale and temporal characteris‐ tics of mental map matching, and the top-down influence of cognitive subjects' knowledge experience on cognitive results. Finally, this paper puts forward the development trend of geospatial cognition entering the new era under the promotion of brain science and artificial intelligence research. [ABSTRACT FROM AUTHOR]

This paper discusses the cognitive connotation of deep mapping. It attempts to consider deep mapping as an interdisciplinary research field, a combination of neuroscience and cartography, and a con‐ nection between brain mapping and geographic mapping. Recent developments in neuroscience, specifically the discovery of spatial cells such as place cells and grid cells, have provided a neuroscientific foundation for studying deep mapping. This paper proposes a conceptual model of deep mapping to establish the relation‐ ship between spatial cell properties and map functions (such as map scale, map measurement, map projec‐ tion, and navigation), and to elucidate the neural mechanism of maps by analyzing an abundance of neuro‐ science literature. This study of deep mapping aims to increase understanding of the nature of maps, enrich cartographic theories, encourage the integration of cartography and artificial intelligence, and expand their applications. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the brain‑like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core is‑ sue is how to improve generalization ability. Methods: This paper introduces the research background and theoretical basis of the brain‑like navigation algorithm. After investigation, we propose a computational framework of brain ‑ like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods. Results： The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain‑like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations. Conclusions: In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability. [ABSTRACT FROM AUTHOR]

Style transfer which originates from computer graphics has attracted broader attention in the field of cartography， considerable efforts have been made on cartographic style transfer algorithms and experimental evaluation. However, it also suffers from unclear demarcation of map style, and lack of evaluation of style transfer results. Therefore, firstly, this paper conceptually analyzes the idea of map style and the applicable scenarios of styled maps. Then, we review existing style transfer methods, and categorize available style transfer methods into three groups and compare them with details: Probability statistics-based, con‐ tent-based, and neural network-based. We also discuss three major types of map style transfer methods: Image to map, remote sensing imagery to map, and image to relief shading. And we compare the advantages and disadvantages of style transfer with vector and raster maps. Finally, we envision the future research of map style transfer in terms of three possible research questions: How to select reference images, how to evaluate style transfer results, and how to integrate style transfer into map design process. [ABSTRACT FROM AUTHOR]

The refined seafloor topography models play an important role in fields of submarine plate tectonic movement, underwater carrier navigation support and marine resource exploration and so on. This paper reviews the development of seafloor topography detection technology and model construction, and discusses the current research status and main challenges of the global refined seafloor topography modeling. Also, the developing trends of global seafloor topography modeling are summarized, considering that the technology of recovering seafloor topography from altimeter-derived marine gravity anomalies is still the main way to construct global seafloor topography models. New altimeter satellites such as dual satellite tandem altimeter and surface water ocean topography will provide data sources for further improving the accuracy of marine gravity field and seafloor topography models. Optimization of seafloor topographic inversion theory based on topographic complexity is expected to bring theoretical innovation. It is worth paying attention to explore the application of artificial intelligence technology in global seafloor topography modeling. [ABSTRACT FROM AUTHOR]

Objectives: Manual seismic phase picking and earthquake catalog building become challenging because the number of seismic stations increases. With the rapid development of artificial intelligence, several deep learning models are developed for seismic phase picking, but their performance and applicability remain ambiguous. Methods: Phase-Net and EqT can extract relevant features at different levels from raw seismic waveforms and map them into probabilistic sequences. In this paper, the performance of these two widely-used deep learning models for earthquake detection and phase picking is investigated with the 2019 Ridgecrest earthquake (California, USA) sequence and the 2019 Weiyuan Ms 5.4 earthquake (Sichuan, China) sequence. Besides, the completeness and accuracy of the constructed earthquake catalogs are analyzed and evaluated by cross-validation. Results: The results show that the two models, though trained with datasets from different regions, perform well in cross-region applications and are comparable to manual analysis. EqT1 provides high detection precision with few false alarms, even under a low threshold. But EqT1 has a limited recall rate, implying that fewer earthquakes are detected compared with Phase-Net. The picking performance of Phase-Net is sensitive to the employed threshold. With the low and moderate thresholds, Phase-Net can detect more earthquakes than EqT1 and manual catalog, while registering a relatively low detection precision. With the high threshold, the precision of Phase-Net is comparable to that of EqT1. EqT2 is not recommended due to its inferior performance over EqT1 and Phase-Net. Conclusions: EqT1 provides a higher precision when being employed for seismic phase picking, leading to earthquake catalogs with better accuracy. Phase-Net has a higher recall rate and generates more complete catalogs. Both models can facilitate automatic and rapid construction of earthquake catalogs. [ABSTRACT FROM AUTHOR]

Objectives: The rapid development of cartographical and communication technologies makes the public free to create, publish, edit and share their map image resources and products with various platforms and tools. These map images are remarkably ubiquitous in terms of map content, mapping standards, and other aspects, which poses a big challenge to establishing massive well-annotated map image data. Thus, although the state⁃of⁃the⁃art deep learning methods have made a breakthrough in recognizing the standardized maps, they are still intrinsically unqualified to effectively address map image recognition and under⁃ standing due to the inadequate well-labeled map images. Methods: This paper summarizes the progress and challenges regarding map image recognition and discusses the theoretical configurations and potential geospatial artificial intelligence (GeoAI) techniques for efficient map image recognition and understanding. We propose the map features for deep learning models to represent map image content. Then, we explore the promising methodologies for map image content recognition and the possible semantic analysis methods for map image understanding. Subsequently, we prospect several implementations regarding map image recognition and understanding and their future potentials. Results: In our opinion, further investigation on theories and methods for map image representation is essential. Moreover, fully utilizing the values of map images depends upon recognizing the explicit content (map image perception) and mining the hidden semantics (map cognition). Conclusions: We hope our exploration can contribute to the cartographical community offering a GeoAI and data representation integrated perspective on map image utilization. [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]