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Advances in Seismic Sensing and Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 559

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Guest Editor
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Interests: seismic imaging; seismic tomography; inverse problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, seismic imaging and tomography techniques have undergone significant advancements, enhancing our ability to probe the Earth's subsurface and understand its complex structure. These methods, coupled with sophisticated inverse problem solving techniques, now allow for more accurate and detailed mapping of geological features and processes.

This Special Issue of Sensors explores the latest developments in seismic sensing and monitoring, focusing on how cutting-edge technologies are revolutionizing our approach to seismic imaging and tomography. From advanced algorithms to novel sensor designs, we highlight the innovations that are pushing the boundaries of what is possible in seismic imaging. Join us as we delve into the exciting world of seismic sensing and its transformative impact on Earth science research and practical applications.

Dr. Qiancheng Liu
Dr. Giovanni Leucci
Guest Editor

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Keywords

  • seismic sensing
  • seismic monitoring
  • seismic imaging and tomography techniques

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Published Papers (1 paper)

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Research

21 pages, 28441 KiB  
Article
Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing
by Xianteng Wang, Xue Li, Zhumei Liu, Zihao Wu, Yike Xie and Zijie Han
Sensors 2025, 25(3), 744; https://doi.org/10.3390/s25030744 - 26 Jan 2025
Viewed by 283
Abstract
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity [...] Read more.
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to calculate the Gestalt factor that characterizes the visual distance. Subsequently, a Delaunay triangular mesh is constructed to create a proximity map between the houses, with the MST generated using visual distance as the weighting factor. Then, the spatial proximity similarity of house clusters is obtained through pruning. Finally, a support vector machine is employed to categorize the architectural structure of the housing complex, viz., simple houses, brick–concrete houses, and frame houses. This classification is based on the geometric, textural, height, and spatial distribution characteristics of the houses. We have conducted a remote sensing classification experiment of house structure types with Zhushan County, Hubei Province as the study area. The results show that the MST clustering method improves the classification accuracy of brick–concrete houses to 95.4% and the classification accuracy of simple houses to 93.4%. Compared to the single-family-based classification method of building structure types, the classification accuracy of frame-structure buildings is improved to 87%. The Kappa coefficient increased to 0.89. This study significantly improves the classification accuracy of building structure types by introducing spatial similarity. Furthermore, it shows the potential for spatial similarity in classifying building structure types. Full article
(This article belongs to the Special Issue Advances in Seismic Sensing and Monitoring)
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