Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations. Many studies use bibliometric data to analyze the popularity of certain research topics, well-adopted methodologies, influential authors, and the interrelationships among research disciplines. However, the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging. This study combined a Space-Time Cube (STC) and a 3D glyph to represent the complex multivariate bibliographic data. We further implemented a visual design by developing an interactive interface. The effectiveness, understandability, and engagement of ST-Map are evaluated by seven experts in geovisualization. The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation, which is a simple and intuitive product with low cost of displaying compared with 3D models. Dimensionality reduction is undoubtedly necessary for line drawings. However, most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex. Therefore, conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection. Based on the given criteria for assessing surface complexity, this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces. A 2D and 3D combined factor that measured the importance of points on describing surface features, vertex weight, was designed. Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions. The feasibility and stability were verified through experiments conducted on real cultural relic surface data. Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces. The extraction method and the obtained results will be useful for line graphic drawing, displaying and propaganda of cultural relics.

In the past two decades, extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data. The introduction of distributed scatterers in Distributed Scatterers InSAR (DS-InSAR) has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points. This study traces the history of DS-InSAR, presents the definition and characteristics of distributed scatterers, and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps: statistically homogeneous pixel selection and phase optimization. Additionally, the latest research progress in this field is tracked and the possible development direction in the future is discussed. Through simulation experiments and two real InSAR case studies, the proposed algorithms are compared and verified, and the advantages of DS-InSAR in deformation measurement practice are demonstrated. This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Floods are one of the most serious natural disasters that can cause huge societal and economic losses. Extensive research has been conducted on topics like flood monitoring, prediction, and loss estimation. In these research fields, flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes. Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time. Deep learning technology has recently shown significant potential in the same field, especially in terms of efficiency, helping to overcome the time-consuming associated with traditional methods. This study explores the potential of deep learning models in predicting flood velocity. More specifically, we use a Multi-Layer Perceptron (MLP) model, a specific type of Artificial Neural Networks (ANNs), to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions. Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training, optimization, and testing of the MLP model. Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time. Meanwhile, we discuss the limitations for the improvement in future work.

Timely acquisition of rescue target information is critical for emergency response after a flood disaster. Unmanned Aerial Vehicles (UAVs) equipped with remote sensing capabilities offer distinct advantages, including high-resolution imagery and exceptional mobility, making them well suited for monitoring flood extent and identifying rescue targets during floods. However, there are some challenges in interpreting rescue information in real time from flood images captured by UAVs, such as the complexity of the scenarios of UAV images, the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform. Thus, we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets (i.e., pedestrians and vehicles trapped by floods). The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model. The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer. Our experimental results demonstrate that the Intersection over Union (IoU) for flood water extraction reaches an impressive 80%, and the IoU for real-time flood water extraction stands at a commendable 76.4%. The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Natural disaster risk monitoring is an important task for disaster prevention and reduction. In the case of immovable cultural relics, however, the feedback mechanism, risk factors, monitoring logic, and monitoring indicators of natural disaster risk monitoring are complex. How to achieve intelligent perception and monitoring of natural disaster risk for immovable cultural relics has always been a focus and a challenge for researchers. Based on the analysis of the concepts and issues related to the natural disaster risk of immovable cultural relics, this paper proposes a framework for natural disaster risk monitoring for immovable cultural relics based on the digital twin. This framework focuses on risk monitoring, including the physical entities of natural disaster risk for immovable cultural relics, monitoring indicators, and virtual entity construction. A platform for monitoring the natural disaster risk of immovable cultural relics is proposed. Using the Puzhou Ancient City Site as a test bed, the proposed concept can be used for monitoring the natural disaster risk of immovable cultural relics at different scales.

Cities hold a critical responsibility for achieving the Sustainable Development Goals (SDGs) due to their high population density, extensive resource consumption, and significant economic contributions. To examine the present state of understandings regarding urban sustainability (SDG 11: Sustainable Cities and Communities) within Chinese research communities, this study collected 15 950 papers from 1994 to 2022 on the 12 indicators of SDG 11, from the China National Knowledge Infrastructure (CNKI), a hub of Chinese academic papers, that directly relate to policymaking. Significant research topics on SDG 11 were identified for each indicator using bibliometrics analysis approaches. The high-frequency keywords and clusters of keywords over the last three decades reveal that existing studies primarily concentrated on the physical aspects, such as transportation and environment, while there is a lack of consideration of societal aspects. This indicates a limited and biased understanding of the urban sustainability within the Chinese academic community. Hence, it is crucial to prioritize the societal aspects in order to develop a research agenda that further advances urban sustainability.