Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing's geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing's transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. © 2023 by the authors.

10th International Conference on Inverse Problems in Engineering ICIPE 22

May 15-19, 2022 Francavilla al Mare (Chieti), ITALYIntroductionThis Proceedings of the Journal of Physics: Conference Series contains a subset of papers presented at the 10th International Conference on Inverse Problems in Engineering (ICIPE), hosted by the Department of Industrial and Information Engineering and Economics, University of L'Aquila, Italy, and held in Francavilla al Mare (Chieti), May 15 – 19, 2022.Due to Coronavirus emergency and to protect the health and safety to all our participants, the 10th Edition, scheduled during May 18-21, 2020, and then May 16-20, 2021, was postponed to May 15-19, 2022, and termed as ICIPE 22.Since the first ICIPE in 1993, this conference has served as the main international venue for collaboration and interaction between applied mathematicians who develop inverse analysis tools, and engineers who use these tools in many different disciplines of science such as manufacturing and machining processes, medical imaging, oil exploration, radar, sonar and seismology, space applications, non-destructive testing and so on. The 2022 meeting continued this tradition, with more than 50 delegates from many sub-disciplines of engineering, science, and applied mathematics. The number of participants was lower than the standard number of 80 – 120 due to both covid travel restrictions in some Asian and American countries and conflict in Ukraine.List of Dedication, References, Organizing Committee, Local Committee, Scientific Committee, List of Registrants, Sponsors and Logos are available in this pdf.

Study on the Simultaneous Measurement of Multiple Positions of the Human Body Using Two-Dimensional Millimeter-Wave Array Radar

Radar-based non contact measurement of physiological signals and vital signs has been of great interest, partly because of the COVID-19 pandemic. Existing studies reported that different physiological signals can be extracted from different positions of the human body. In this study, we demonstrate the measurement of multiple positions of the human body using a radar system with a two-dimensional antenna array. Using a 79-GHz 48-channel multiple-input multiple-output antenna array, we image multiple body parts of participants and separate the echoes using array signal processing. We present experimental results to show the feasibility of the proposed approach. © 2022 The Institute of Electronics Information and Communication Engineers (IEICE) of Japan.

Assessing Coastal Inundation due to a Transboundary Cyclone Using Sentinel 1 SAR Imagery

Cyclone Amphan made landfall on the Indian state of West Bengal on May 20, 2020, during the initial phase of the COVID-19 pandemic. It passed through the western edge of the Sundarbans mangrove forest following a north-north-eastern track affecting neighboring Bangladesh as well. The overlapping of this cyclone during the pandemic made emergency response especially challenging for two of the most densely populated countries in the world. Nevertheless, remote sensing has been extremely useful in such scenarios where accessibility and in situ data-sharing are compromised. The application of multispectral satellite imagery is especially common in large-scale impact assessment following natural events, but the presence of clouds during cyclones makes these images often less effective. Sentinel 1 synthetic-aperture radar (SAR) imagery thus can be very useful due to its cloud penetration capability. This study shows that this freely available global data can provide back-of-the-envelope damage assessment using minimal computational facilities. Therefore, the objective is to perform an inundation assessment in coastal districts (level-2 administrative area) of India and Bangladesh due to Cyclone Amphan using Sentinel 1 SAR imagery. © ASCE.

milliTRACE-IR: Contact Tracing and Temperature Screening via mmWave and Infrared Sensing

Social distancing and temperature screening have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with these aspects separately, their combination would greatly benefit the continuous monitoring of public spaces and help trigger effective countermeasures. This work presents milliTRACE-IR, a joint mmWave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. milliTRACE-IR combines, via a robust sensor fusion approach, mmWave radars and infrared thermal cameras. It achieves fully automated measurement of distancing and body temperature, by jointly tracking the subjects's faces in the thermal camera image plane and the human motion in the radar reference system. Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars. When entering a new room, a subject is re-identified among several other individuals by computing gait-related features from the radar reflections through a deep neural network and using a weighted extreme learning machine as the final re-identification tool. Experimental results, obtained from a real implementation of milliTRACE-IR, demonstrate decimeter-level accuracy in distance/trajectory estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5 degrees C). Furthermore, milliTRACE-IR provides contact tracing through highly accurate (95%) person re-identification, in less than 20 seconds.

ANALYZING THE SITUATIONAL AND EVENT-DEPENDENT MARITIME TRAFFIC VARIATIONS USING COSMO-SKYMED SAR IMAGERY IN WUHAN, CHINA, BEFORE AND DURING COVID-19 LOCKDOWN

Vessel detection and their activities in the sea can provide updates on latest trends in maritime trade. Space-borne synthetic aperture radar (SAR) can aid in detecting vessels in (almost) all weather conditions. In this study high resolution SAR data are used to analyze the maritime traffic activities, especially the underscored independency in transport trends in Wuhan, the major port-hub on the central Yangtze river in China, before and during the COVID-19 pandemic. Time-series of COSMO-SkyMed SAR images covering Wuhan from 2018 to 2020 were exploited to detect vessels. We applied multi-mode feature and shape (MMFS) image enhancement for fast and accurate vessel detection. Variations in number of vessels were detected, especially a huge drop was observed during the COVID-19 lockdown. HwkEye360 radio frequency monitoring data were used to validate our results. © 2021 IEEE

High-Resolution Radar Sensing Sea Surface States During AMK-82 Cruise

Every year situation when the Arctic seas are free of ice is becoming more frequent. It allows scientists to study hard-to-reach areas using well-equipped research vessels instead of icebreakers. During the Covid-19 pandemic, the successful expedition of the research vessel Academician Mstislav Keldysh with more than 60 scientists from 15 countries across the four Arctic seas (Barents, Kara, Laptev, and East Siberian) on September - November 2020 seems like a real wonder. One of the expedition tasks was remote sensing of different hydrophysical processes by their manifestation on the sea surface using marine radar. The present paper proposes the method of generating high spatial resolution radar maps of the sea surface and algorithms of hydrophysical processes identification. This paper also presents examples of registered processes such as wind waves, ice fields with different types of ice (grease ice, pancake ice, nilas, and young ice), manifestations of internal waves observed in the Kara Gate and Vilkitsky Strait, as well as manifestations of intense methane seeps on the sea surface. This paper contains quantitative estimations of the physical parameters of the observed processes underlying the effectiveness of Doppler marine radars in harsh conditions of the Arctic seas. Author

Land cover change in Badung Regency 2016-2020 : An approach using machine learning method: Random Forest Extreme Gradient Boost (XGB)

Badung Regency is one area that mostly suffered from Covid-19 pandemic. Their gross regional domestic product has decreased 21.5% from 2019 to 2020 because of sluggishness of the tourism sector. It also affects the physical development of Badung Regency as a fast-changing area. To map the change of its land cover, satellite imagery-based classification was conducted. Both optical and radar imagery has its own deficiencies due to cloud cover in optical imagery and difficulties in interpretation in radar imagery. Therefore, combining optical and radar imagery and classifying the land cover through machine learning (ML) algorithm is necessary. In this study, we compare two methods of ML which are Random Forest and Extreme Gradient Boost. Sentinel 1 and 2 imageries utilized as the input to derive land cover change from 2016 to 2020. The data is classified into five classes: dense vegetation, sparse vegetation, bare land, water body, and urban, using supervised classification. As for training and validation, the field survey data was conducted. With similar input and set of training data, Extreme Gradient Boost (XGB) methods yield higher average accuracy than Random Forest (RF). The XGB has around 93% of accuracy, while RF has around 76% accuracy. From the result of land cover change using XGB method, bare land and water bodies are decreasing 22.9% and 4.1% consecutively. While urban areas and sparse vegetation, slightly develop around 5.6% and 1.26%. Dense vegetation has almost not changed with increasing 0.34% of its area. © 2021 IEEE.