Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

Huang, Guanwen; Wang, Duo; Du, Yunyan; Zhang, Qin; Bai, Zhengwei; Wang, Chun

doi:10.3389/feart.2022.884500

Cited by 17 publications

(3 citation statements)

References 23 publications

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“…Integration of part into T_set tuple (19) else if (20) Integration of intersect (part, IP) into I_set tuple (21) end if (22) end for (23) are mainly used in the study as the main factors to measure the temporal characteristics. However, the window size and input threshold set in the sliding window algorithm also have an impact on the fnal compression efect [36,37].…”

Section: Trajectory Compression Of Critical Regions Considering Spati...mentioning

confidence: 99%

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Zhang,

Zhou

2023

Journal of Advanced Transportation

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Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.

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Section: Trajectory Compression Of Critical Regions Considering Spati...mentioning

confidence: 99%

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Zhang,

Zhou

2023

Journal of Advanced Transportation

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“…Although GNSS measurements for topographic monitoring purposes have been used since the 1990s, the advances in technology and computer science prove that it is still an active field of research. Specifically, different GNSS-based algorithms were developed in order to precisely detect displacements [30], while the adaptive sliding window method was proposed to provide reliable and operational landslide information [31]. Furthermore, low-cost GNSS receivers have been manufactured to evaluate surface deformation in real-time [32,33].…”

Section: Introductionmentioning

confidence: 99%

UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece

Nikolakopoulos,

Kyriou,

Koukouvelas

et al. 2023

Remote Sensing

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Areas in Western Greece are particularly prone to landslides. Usually triggered by earthquakes or intense rainfalls, they cause damage to infrastructure (roads, bridges, etc.) and human properties. Hence, there is an urgent need for the implementation of monitoring and landslide prevention methodologies. In the last years, Unmanned Aerial Vehicles (UAVs), Global Navigation Satellite Systems (GNSS), and Interferometric SAR (InSAR) techniques have been applied for landslide mapping and monitoring. The current study focuses on the systematic and long-term analysis of a landslide that occurred in Ano Kerassovo village, within the region of Western Greece. To precisely measure the current evolution of the landslide, we performed repetitive UAV campaigns in conjunction with corresponding GNSS surveys, covering a time period between February 2021 and April 2023. The identification of surface modification was based on a change detection approach between the generated point clouds. The results are validated through GNSS measurements and field observations. Added to this, we collected archived Persistent Scatterer Interferometry (PSI) measurements derived from the European Ground Motion Service (EGMS) to extend the observation period and gain a more complete understanding of the phenomenon. It is proven that archived PSI measurements can be used as an indicator of possible landslide initialization points and for small-scale large coverage investigations, while UAVs and GNSS data can precisely identify the microscale deformations (centimeter scale).

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“…GNSS monitoring data for landslides, bridges, and oil rigs are transmitted on the internet, by tradition. Landslide monitoring usually sets several GNSS monitoring stations on a side slope or geological hazard susceptibility slope to obtain GNSS observations for deformation and sedimentation monitoring [ 1 , 2 ]. Bridge monitoring uses three to more than ten stations on different parts of a bridge to monitor its dynamic deformation and extract its structural health characteristics [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Data and Service Security of GNSS Sensors Integrated with Cryptographic Module

Zhang

et al. 2023

Micromachines

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Navigation and positioning are of increasing importance because they are becoming a new form of infrastructure. To ensure both development and security, this study designed a technical innovation structure to upgrade the GNSS (Global Navigation Satellite System) data transmission and real-time differential correction service system and proposed a new multiple cryptographic fusion algorithm to achieve the encryption and decryption of GNSS data and services. First, a GNSS station encrypts GNSS data with an encryption key and obtains a public key from a GNSS data center to encrypt the GNSS data encryption key. After that, identity authentication of a GNSS station is carried out, and an SSL VPN is established between the GNSS station and a GNSS data center before GNSS data are transmitted to the GNSS data center. Then, the GNSS data center decrypts the received GNSS data. The process of an intelligent terminal for real-time differential corrections is similar to that of the GNSS station and the GNSS data center. A GNSS sensor integrated with a cryptographic module was developed to validate the structure in an open environment. The results showed that the developed GNSS sensor was successful in encrypting the data, and the GNSS data center was able to decrypt the data correctly. For the performance test, a cryptography server was able support the requirements of GNSS applications. However, a cryptography server was optimal in supporting 40~50 GNSS stations simultaneously, whereas a cluster was suggested to be configured if the number of GNSS stations was more than 60. In conclusion, the method was able to ensure the validity, confidentiality, integrity, and non-repudiation of GNSS data and services. The proposed upgrading technology was suitable for coordinating GNSS development and security.

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Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

Cited by 17 publications

References 23 publications

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

UAV, GNSS, and InSAR Data Analyses for Landslide Monitoring in a Mountainous Village in Western Greece

Data and Service Security of GNSS Sensors Integrated with Cryptographic Module

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