sensors-logo

Journal Browser

Journal Browser

Application of Remote Sensing in Earthquake-Induced Geological Hazard and Building Damage

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1441

Special Issue Editors

Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: natural hazards; remote sensing technology; machine learning for landslide susceptibility assessment; UAV

E-Mail Website
Guest Editor
1. Institute of Geology, China Earthquake Administration, Beijing 100029, China
2. Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China
Interests: geological hazards; engineering geology; active fault; geohazard risk assessment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
Interests: spatial analysis; remote sensing; engineering geology; landslides; natural hazards
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: landslide susceptibility mapping; GIS; Seismic risk evaluation; landslide hazard assessment

E-Mail Website
Guest Editor
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: seismic building damage detection using optical remote sensing image; seismic risk assessment; thermal infrared remote sensing

Special Issue Information

Dear Colleagues,

With the rapid advancements of remote sensing and geographic information systems (GIS), especially the widespread adoption of high-precision Earth observation techniques, sensors, and large-scale data acquisition, significant progress has been made in addressing scientific issues related to earthquake-induced geological disasters and building damage. On one hand, these technological developments have established themselves as crucial sources of data for disaster assessment and mechanistic analysis, gradually replacing traditional field survey methods. They have become a valuable and cost-effective resource for geologists in the establishment of seismic damage databases. On the other hand, the integrated use of various cutting-edge technologies, such as high-resolution satellite remote sensing, unmanned aerial vehicle (UAV) surveys, and airborne laser scanning, allows for the acquisition of multisource observational data for seismic disasters across large regions, at different levels, and across various spatiotemporal scales. This diverse set of observational data, obtained through the different technological means serves as valuable input for subsequent analysis by researchers, offering a unique opportunity for the rapid and comprehensive disaster loss assessment.

This Special Issue is dedicated to the theme of "remote sensing in geological hazard and building damage". It aims to provide a forum for original research that explores new frontiers and challenges in the applications of remote sensing for earthquake-induced geological disasters and building damage. Moreover, innovative methods and original applications, including but not limited to earthquake-induced geohazard prediction, recognition, formation mechanism, susceptibility mapping, risk management, and building damage, would be highly appropriate for inclusion.

Topics of interest include, but not limited to, the following:

  • The database of landslides related to extreme events or mountainous areas;
  • Physics-based and data-driven landslide susceptibility mapping;
  • The post-failure evolution and prediction of geohazards both temporally and spatially using remote sensing techniques;
  • Damage detection based on UAV and satellite remote sensing;
  • Damaged buildings based on visible, thermal infrared, SAR, and Lidar sources

Dr. Siyuan Ma
Prof. Dr. Renmao Yuan
Dr. Xiaoyi Shao
Prof. Dr. Xiaoli Chen
Dr. Xiwei Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural hazards
  • remote sensing
  • landslides
  • earthquake

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 10870 KiB  
Article
An Improved Instance Segmentation Method for Fast Assessment of Damaged Buildings Based on Post-Earthquake UAV Images
by Ran Zou, Jun Liu, Haiyan Pan, Delong Tang and Ruyan Zhou
Sensors 2024, 24(13), 4371; https://doi.org/10.3390/s24134371 - 5 Jul 2024
Cited by 1 | Viewed by 1000
Abstract
Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results. However, for high-resolution Unmanned Aerial Vehicle (UAV) imagery, [...] Read more.
Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results. However, for high-resolution Unmanned Aerial Vehicle (UAV) imagery, these methods may result in the problem of various damage categories within a building and fail to accurately extract building edges, thus hindering post-disaster rescue and fine-grained assessment. To address this issue, we proposed an improved instance segmentation model that enhances classification accuracy by incorporating a Mixed Local Channel Attention (MLCA) mechanism in the backbone and improving small object segmentation accuracy by refining the Neck part. The method was tested on the Yangbi earthquake UVA images. The experimental results indicated that the modified model outperformed the original model by 1.07% and 1.11% in the two mean Average Precision (mAP) evaluation metrics, mAPbbox50 and mAPseg50, respectively. Importantly, the classification accuracy of the intact category was improved by 2.73% and 2.73%, respectively, while the collapse category saw an improvement of 2.58% and 2.14%. In addition, the proposed method was also compared with state-of-the-art instance segmentation models, e.g., Mask-R-CNN and YOLO V9-Seg. The results demonstrated that the proposed model exhibits advantages in both accuracy and efficiency. Specifically, the efficiency of the proposed model is three times faster than other models with similar accuracy. The proposed method can provide a valuable solution for fine-grained building damage evaluation. Full article
Show Figures

Figure 1

Back to TopTop