New Progress of Building and Engineering Structure Health Monitoring Based on Remote Sensing Technology
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 351
Editors
Interests: remote sensing; earthquake damage assessment; computer vision; deep learning; UAV remote sensing
Interests: computer vision; deep learning; change detection; UAV remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing technology has driven the transformation of building and engineering structure health monitoring from traditional point-based sensing to a comprehensive, continuous, and intelligent method, and has promoted non-contact and cost-effective assessment models. High-resolution optical cameras, LiDAR, and unmanned aerial vehicle platforms enable flexible inspections of building facades and infrastructure, while satellite synthetic aperture radar interferometry (InSAR) provides large-scale millimeter-level deformation monitoring and microscopic damage identification. Simultaneously, artificial intelligence technologies—including machine learning, deep learning, and data fusion—have further enhanced the application of multi-source remote sensing data in damage identification, deformation prediction, and risk assessment.
This Special Issue aims to collect articles on new methods, practical applications, and innovative frameworks for monitoring building and engineering structural health using remote sensing technology, promoting the scientific basis and practical application of remote sensing in structural health monitoring. The research topics include, but are not limited to, the following:
- Comprehensive structural health monitoring integrating satellite, airborne, ground, and on-site sensor data;
- Satellite and ground-based radar interferometry for deformation monitoring and spatiotemporal evolution modeling of buildings and infrastructure;
- Applications of artificial intelligence and data-driven methods in automatic damage classification, anomaly detection, and deformation prediction;
- Unsupervised/weakly supervised structural change detection based on transformer and diffusion model;
- Deep feature alignment and fusion networks for multimodal remote sensing data (optical, radar, thermal infrared);
- BIM integration and structural health assessment framework based on remote sensing observations.
Dr. Shumin Wang
Prof. Dr. Zongxu Pan
Dr. Haiming Zhang
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing 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 2700 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
- structural health monitoring
- multi-source remote sensing data
- InSAR
- damage identification
- deformation prediction
- artificial intelligence
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