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Objectives: The 5 September 2022 Mw 6.6 Luding earthquake is the largest earthquake occurring on the Xianshuihe fault, Eastern Tibet in the past 40 years, and is of great significance for investigating the tectonic activity and assessing future seismic hazards in the region. Methods: In this study, we used Sentinel-1 and ALOS-2 synthetic aperture radar (SAR) images to retrieve coseismic surface displacements and then to determine the fault geometry parameters and slip distribution with a dislocation model in an elastic half-space. Results: Our results show that the earthquake is a left-lateral sliding event with a maximum LOS surface displacement of ~15 cm and ~21 cm for the Sentinel-1 and ALOS-2 radar images, respectively. Conclusions: The fault ruptured along an NNW-SSE strike, and westward at a dip of 72°. The slip was concentrated at depths of 0-10 km with a maximum fault slip of 2.23 m occurring at a depth of ~5.8 km. By analyzing the distribution of the coseismic landslides interpreted by optical images, it is found that the coseismic landslides were mainly located on the west side of the fault, which is consistent with the aftershock distribution and can be considered due to hanging wall effects.
Objectives: An earthquake with the magnitude of Ms 6.8 struck Luding County,Ganzi Prefecture,Sichuan Province on September 5,2022,with the epicenter about 10 km away from Hailuogou Glacier.How Hailuogou Glacier was affected by the earthquake has been widely concerned by the society. Methods: Firstly,the glacier area is monitored based on normalized difference snow index using multi temporal Landsat 8 and Sentinel-2 optical satellite images.Secondly,Sentinel-1 synthetic aperture radar satellite image is used to monitor the displacement before and during the Luding earthquake based on pixel offsettracking technology. Results: The results indicate that the area of Hailuogou Glacier shows a shaking trend from 2016 to 2022,which have a negative correlation with the daily average maximum temperature.While the velocity has a positively correlation with the slope gradient and the daily average maximum temperature.The Luding earthquake did not cause a significant increase in the velocity of Hailuogou Glaciers in a large range,but it significantly disturbed the front area of the ice waterfall. Conclusions: The possibility of direct disaster caused by ice avalanches after the earthquake was low,but which increased the risk of disaster caused by debris flow.
At 12:52 on September 5, 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province. This event triggered extensive geological hazards in mountainous areas, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of induced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtains the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle(UAV) and satellite remote sensing images, machine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, integrating these landslide data, the model of was optimized afterwards, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. The results show that the coseismic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geological hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital information of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake.
Objectives In the remote sensing (RS) big data era, intelligent interpretation of remote sensing images (RSI) is the key technology to mine the value of big RS data and promote several important applications. Traditional knowledge-driven RS interpretation methods, represented by expert systems, are highly interpretable, but generally show poor performance due to the interpretation knowledge being difficult to be completely and accurately expressed. With the development of deep learning in computer vision and other fields, it has gradually become the mainstream technology of RSI interpretation. However, the deep learning technique still has some fatal flaws in the RS field, such as poor interpretability and weak generalization ability. In order to overcome these problems, how to effectively combine knowledge inference and data learning has become an important research trend in the field of RS big data intelligent processing. Generally, knowledge inference relies on a strong domain knowledge base, but the research on RS knowledge graph (RS-KG) is very scarce and there is no available large-scale KG database for RSI interpretation now. Methods To overcome the above considerations, this paper focuses on the construction and evolution of the RS-KG for RSI interpretation and establishes the RS-KG takes into account the RS imaging mechanism and geographic knowledge. Supported by KG in the RS field, this paper takes three typical RSI interpretation tasks, namely, zero-shot RSI scene classification, interpretable RSI semantic segmentation, and large-scale RSI scene graph generation, as examples, to discuss the performance of the novel generation RSI interpretation paradigm which couples KG and deep learning. Results and Conclusions A large number of experimental results show that the combination of RS-KG inference and deep data learning can effectively improve the performance of RSI interpretation.The introduction of RS-KG can effectively improve the interpretation accuracy, generalization ability, anti-interference ability, and interpretability of deep learning models. These advantages make RS-KG promising in the novel generation RSI interpretation paradigm.
High-precision indoor positioning technology is of great significance to the development of national economy.In recent years, with the increasing demand for location services and the continuous iteration of technology, indoor positioning continues to develop towards high precision and seamless. In the case that the satellite signal cannot cover the indoor, the high-precision indoor positioning technology has become a research hotspot, and a variety of positioning sources and corresponding positioning principles have been developed. Aiming at the latest development status of high-precision indoor positioning technology, we divide into high-precision positioning methods based on geometric relation, fingerprint matching, incremental estimation and quantum navigation according to different positioning principles. We introduce the positioning principle of various methods, discuss and analyze the current technology development, summary the characteristics of high-precision indoor positioning technology, and discuss the future development trend of multi-source, intelligent and popular. Through the analysis of different positioning principles and technologies, it has reference value for the future study of indoor high-precision positioning sources and fusion positioning methods of different positioning sources.
Objectives As an emerging airborne remote sensing system, unmanned aerial vehicle (UAV) falls into multiple categories with various payloads, such as the multi‐rotor and fixed‐wing ones. Despite the harsh climatic condition, UAV is widely used in many Antarctic fields of basic and applied science, which still lacks a comprehensive and systematic literature review. Methods We firstly discuss the special impact of Antarctic environmental conditions (meteorology, electromagnetic field, light, etc.) on the UAV operation. A comprehensive literature retrieval is subsequently presented on the current application of UAV in Antarctic research and expedition. We sort out 104 papers according to the time of publication, main journals, study areas, nations, and institutions. Representative literature is reviewed in seven application areas, including aeromechanics, atmosphere, sea ice and iceberg, glacier, geomorphology and geomagnetism, ecology‐vegetation as well as ecology‐animals. We retrospect the development and achievement of UAV's applications in the Chinese national Antarctic research and summarize the limitations of the research on the application of UAV in Antarctica. Results A number of environmental factors need to be considered before the UAV missions, such as the meteorological conditions, electromagnetic field, solar radiations, and flight regulations. According to the review, half of the literature belongs to the journal paper, mostly in Polar Biology, Polar Science, and Remote Sensing. The earliest UAV research in Antarctica was published in 2004, followed by a productive period of International Polar Year in 2008. The first‐tier countries including the USA, Australia, and Germany, led the progress in the research on UAVs in Antarctica. Meanwhile, the dominant role of top universities stood out via various collaborations. The UAV can also be classified into multiple categories according to the payload, such as the industrial‐ or consumer‐grade optical cameras, radiosonde, synthetic aperture radar, and light detecting and ranging(LiDAR), among which consumer‐grade camera is widely used in Antarctic investigations. China's Antarctic expedition team initiated the Antarctic UAV program in 2007 and had carried out at least 18 flight missions by 2020. The flights covered Zhongshan Station, The Great Wall Station, Inexpression Island, and the inland ice sheet, from which the collected data were employed to support the glaciological, geomorphic, and biological studies. Conclusions The UAV remote sensing, as the essential technology in the"Air‐Space‐Ground" polar observation system, has been increasingly upgraded in the recent decade. The flight experiments covered the primary research topics and research fields in Antarctic science. In general, the application and development of Antarctic UAV in China lie in the second tier, falling behind the USA and Australia. In the end, according to the current development of Antarctic UAV in China, this paper provides guidance for China's Antarctic expedition team in the future: (1) Develop new UAV models; (2) Make breakthroughs in the battery technology; (3) Couple multiple sensors; (4) Encourage trans‐disciplinary collaboration; (5) Promote foreign communication and sharing; (6) Participate in the international management.
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 understanding 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.
Objectives Flatness inspection during construction period is critical to ensure the floor perform its desired function after completion. There are some shortages in traditional floor flatness measurement methods based on straight edge or levelling instrument, for example, not all locations in area can be measured and the measurement speed is slow, so that these methods can not fit the fast and accurate flatness measuring requirement of oversized floor during construction. Methods According to the features of target floor, which has large area and is not entirely solid, we present a rapid measuring and calculating method for floor flatness based on inertial navigation system and total station. First, we collect inertial data by inertial measurement sled, and observe the coordinates of the sled by total station. Second, we use Kalman filter to fuse inertial data and the unit̓s coordinates to solve locations and attitudes of trajectory points. Third, we calculate the flatness indicator according to the elevation of trajectory points. Results Experimental results show that the proposed method has nearly equal accuracy comparing to levelling instrument, with significant improvement in speed performance. Conclusions The proposed method can not only make flatness quality assessment for floor under construction, but also detect and locate the flatness abnormalities so that provide direction for floor grinding. This method has great practical value and a wide application prospect.
Objectives Nowadays, a vast number of sensors and observation platforms bring a significant challenge for city perception. In the study, we focus on developing of a smart city tempo-spatial perception system. The concept of smart city awareness base station (SCABS) is proposed in this study, and the SCABS architecture considers the above problems. Methods Firstly, we systematically reviewed the requirements and the complexity of smart city perception and summarized the development process and network architecture of smart city perception technology systems. On this basis, the challenges of ubiquitous access, trusted perception and intelligent management in future smart city perception are addressed. Then, this paper sorted out the demand for future city perception services and constructed the architecture of SCABS. Finally, we standardized the technical indicators and service capabilities of SCABS. Results We constructed the prototype of SCABS. According to the qualitative analysis and experiments based on real environment, we found that SCABS with unified access, integrated management, and edge intelligence can meet the access and management ability of multi-platform and high-density sensing resources. Conclusions SCABS would act an essential role in the smart city perception in the future. As a cyber-physical infrastructure, it expects to cover various scenarios in the city with multiple heterogeneous perception access and hybrid access of multi-protocols, and to automatically support the multi-platform coordinated control. The SCABS meets global, accurate, heterogeneous, and multiple sensory perception. The SCABS is expected to be one of the critical infrastructures of the future smart city.
The rapid progress of urbanization calls for advanced urban governance models which can improve public safeties and the operational efficiency of a city. Based on urban sensing technology and urban big data platforms, the unified urban governance models connect different systems in different government departments so that the instructions from one department can be directed to other departments. As a result, different government departments can work together to solve a problem which cannot be handled by an individual department. In addition, the unified urban governance models leverage digital twins techniques to bridge the gap between the physical world and the virtual world, enabling government officers to solve real urban challenges just like playing a video game.
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