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Advanced Sensing Systems for Building Inspection, Visualization and Assessment
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Advanced Sensing Systems for Building Inspection, Visualization and Assessment

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1486

Editors

Prof. Dr. Yue Fan

E-Mail Website
Guest Editor
School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
Interests: sustainable housing; AI-driven building detection; human-centered design; existing building renovation; neighborhood regeneration
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Xue

E-Mail Website
Guest Editor
School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
Interests: green building; urban diagnostic; healthy built environment; climate-response design
Dr. San Jiang

E-Mail Website
Guest Editor
Department of Urban Spatial Information Engineering, Shenzhen University, Shenzhen 518060, China
Interests: photogrammetry; image matching; 3D reconstruction; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative sensing technologies and intelligent systems for building condition assessment, defect detection, and heritage preservation. We invite contributions on multi-modal sensor integration (visible, thermal, LiDAR, hyperspectral, etc.,), AI-enhanced analysis, digital twin visualization, and automated inspection frameworks that advance building maintenance practices and smart city infrastructure development. Core research areas and topics include, but are not limited to, the following:

  • Automated inspection platforms (UAV swarms, robotic systems, mobile mapping system);
  • Multi-modal building sensing (Visible/thermal/ LiDAR/ hyperspectral imaging);
  • AI-driven defect detection (deep learning for crack, moisture, material degradation detection);
  • 3D reconstruction and digital twins for building modeling and envelope assessment;
  • Real-time monitoring systems for ageing infrastructure;
  • Heritage building preservation through advanced imaging;
  • Energy performance assessment via façade thermal analysis;
  • Non-destructive testing technologies (ground-penetrating radar, acoustics, vibration sensors).

Prof. Dr. Yue Fan
Dr. Fei Xue
Dr. San Jiang
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. 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

  • UAV-based building inspection
  • multi-modal sensing for buildings
  • deep learning for defect detection
  • building envelope assessment
  • crack and moisture detection
  • 3D building reconstruction
  • parametric visualization for building diagnostics
  • smart building maintenance systems
  • digital twins for existing buildings
  • heritage conservation technologies

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Published Papers (3 papers)

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Research

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29 pages, 47643 KB  
Article
Integrating Multi-Temporal UAV Thermal Imaging and 3D Path Planning for Facade Thermal Defect Diagnosis in Old Residential Buildings
by Senhong Cai, Xuetong Li and Zhonghua Gou
Sensors 2026, 26(14), 4385; https://doi.org/10.3390/s26144385 - 10 Jul 2026
Abstract
Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, [...] Read more.
Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, this study proposes an integrated diagnostic framework for old residential buildings in Wuhan, China, combining unmanned aerial vehicle (UAV) infrared thermography, multi-temporal data acquisition, 3D flight-path planning, thermal anomaly recognition, facade spatial mapping, and temporal screening. Field experiments were conducted to determine key acquisition parameters, including sensor preheating time, imaging distance, and acquisition timing. Thermal anomalies were identified through image-processing techniques and mapped onto facade representations derived from 3D models. Repeated observations across different times and days were then used to evaluate anomaly recurrence and spatial stability. The results show that preheating the sensor for at least 10 min, maintaining a UAV-to-facade distance of 8–10 m, and acquiring data around 17:00 provide more reliable thermal images. Multi-temporal screening effectively reduces false positives caused by temporary disturbances, while persistent anomalies associated with window–wall joints, floor slabs, wall surfaces, and moisture-related areas can be identified more robustly. The proposed framework provides a practical workflow for facade thermal defect diagnosis and retrofit-oriented decision support. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Building Inspection, Visualization and Assessment)
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29 pages, 5889 KB  
Article
An Indoor Accessibility Assessment Framework Based on Multimodal Sensing and Explainable Machine Learning: A Case Study of a Tactile Museum for People with Visual Impairments
by Yiqi Tao, Zhiheng Guo, Yusong Zhu, Jingyi Zhang, Zhaohui Yang, Yejin Wang, Yijia Chen, Yuxi Zhou and Fang Liu
Sensors 2026, 26(13), 4198; https://doi.org/10.3390/s26134198 - 2 Jul 2026
Viewed by 234
Abstract
As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor [...] Read more.
As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Building Inspection, Visualization and Assessment)
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Review

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37 pages, 10719 KB  
Review
UAV and Deep Learning for Building Façade Defect Detection: A Comprehensive Review
by Yue Fan, Yuheng Deng, Fei Xue, Jinghua Mai, Stephen Siu Yu Lau and Chi Ho Li
Sensors 2026, 26(12), 3959; https://doi.org/10.3390/s26123959 - 22 Jun 2026
Viewed by 715
Abstract
Unmanned aerial vehicles (UAVs) and deep learning (DL) have introduced a new framework for intelligent building façade defect detection, yet existing studies often focus on isolated technical components and lack a systematic evaluation of the entire pipeline. To address this gap, this paper [...] Read more.
Unmanned aerial vehicles (UAVs) and deep learning (DL) have introduced a new framework for intelligent building façade defect detection, yet existing studies often focus on isolated technical components and lack a systematic evaluation of the entire pipeline. To address this gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles retrieved from the Web of Science database over the period 2021–2026. This review investigates four key domains: (1) UAV inspection path planning and data acquisition; (2) multi-modal data fusion; (3) DL-driven defect detection algorithms; and (4) 3D reconstruction and digital twin integration. Our analysis reveals the following main findings. Real-time perception-aware planning is central to UAV path planning, yet most studies lack robustness evaluations under real-world deployment conditions. Multi-modal data fusion improves detection across multiple defect types, yet edge deployment requires balancing lightweight design with recognition stability. Defect recognition algorithms increasingly adopt task-driven architectures, but limited edge-device resources demand joint optimization of efficiency and accuracy. In digital twins, systematic research is still lacking on semantically integrating recognition results into BIM for O&M decision-making, leaving the closed loop from defect detection to maintenance unresolved. This review aims to help researchers and practitioners advance UAV-based inspection from an auxiliary tool to a fully autonomous, reliable intelligent agent for refined management of the urban built environment. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Building Inspection, Visualization and Assessment)
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