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Keywords = aerial thermography

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36 pages, 53355 KiB  
Article
Making the Invisible Visible: The Applicability and Potential of Non-Invasive Methods in Pastoral Mountain Landscapes—New Results from Aerial Surveys and Geophysical Prospection at Shielings Across Møre and Romsdal, Norway
by Kristoffer Dahle, Dag-Øyvind Engtrø Solem, Magnar Mojaren Gran and Arne Anderson Stamnes
Remote Sens. 2025, 17(7), 1281; https://doi.org/10.3390/rs17071281 - 3 Apr 2025
Viewed by 1602
Abstract
Shielings are seasonal settlements found in upland pastures across Scandinavia and the North Atlantic. New investigations in the county of Møre and Romsdal, Norway, demonstrate the existence of this transhumant system by the Viking Age and Early Middle Ages. Sub-terranean features in these [...] Read more.
Shielings are seasonal settlements found in upland pastures across Scandinavia and the North Atlantic. New investigations in the county of Møre and Romsdal, Norway, demonstrate the existence of this transhumant system by the Viking Age and Early Middle Ages. Sub-terranean features in these pastoral mountain landscapes have been identified by remote sensing technologies, but non-invasive methods still face challenges in terms of practical applicability and in confirming the presence of archaeological sites. Generally, aerial surveys, such as LiDAR and image-based modelling, excel in documenting visual landscapes and may enhance detection of low-visibility features. Thermography may also detect shallow subsurface features but is limited by solar conditions and vegetation. Magnetic methods face challenges due to the heterogeneous moraine geology. Ground-penetrating radar has yielded better results but is highly impractical and inefficient in these remote and rough landscapes. Systematic soil coring or test-pitting remain the most reliable options for detecting these faint sites, yet non-invasive methods may offer a better understanding of the archaeological contexts—between the initial survey and the final excavation. Altogether, the study highlights the dependency on landscape, soil, and vegetation, emphasising the need to consider each method’s possibilities and limitations based on site environments and conditions. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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20 pages, 20397 KiB  
Article
Assessing Seasonal and Diurnal Thermal Dynamics of Water Channel and Highway Bridges Using Unmanned Aerial Vehicle Thermography
by Abdulkadir Memduhoğlu and Nizar Polat
Drones 2025, 9(3), 205; https://doi.org/10.3390/drones9030205 - 13 Mar 2025
Viewed by 737
Abstract
Bridges are critical components of modern infrastructure, yet their long-term performance is often compromised by thermal stresses induced by environmental and material factors. Despite advances in remote sensing, characterizing the complex thermal dynamics of bridge structures remains challenging. In this study, we investigate [...] Read more.
Bridges are critical components of modern infrastructure, yet their long-term performance is often compromised by thermal stresses induced by environmental and material factors. Despite advances in remote sensing, characterizing the complex thermal dynamics of bridge structures remains challenging. In this study, we investigate the seasonal and diurnal thermal behavior of two common bridge types—a water channel bridge with paving stone surfacing and a highway bridge with asphalt surfacing—using high-resolution UAV thermography. A pre-designed photogrammetric flight plan (yielding a ground sampling distance of <5 cm) was implemented to acquire thermal and visual imagery during four distinct temporal windows (winter morning, winter evening, summer morning, and summer evening). The methodology involved generating thermal orthophotos via structure-from-motion techniques, extracting systematic temperature measurements (n=150 per bridge), and analyzing these using independent-samples and paired t-tests to quantify material-specific thermal responses and environmental coupling effects. The results reveal that the water channel bridge exhibited significantly lower thermal variability (1.54–3.48 °C) compared to the highway bridge (3.27–5.66 °C), with pronounced differences during winter mornings (Cohen’s d=2.03, p<0.001). Furthermore, material properties strongly modulated thermal dynamics, as evidenced by the significant temperature differentials between the paving stone and asphalt surfaces, while ambient conditions further influence surface–ambient coupling (r=0.961 vs. 0.975). The results provide UAV-based quantitative metrics for bridge thermal assessment and empirical evidence to support the temporal monitoring of bridges with varying materials and environmental conditions for future studies. Full article
(This article belongs to the Special Issue Unconventional Drone-Based Surveying 2nd Edition)
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53 pages, 50379 KiB  
Review
Sensing Techniques for Structural Health Monitoring: A State-of-the-Art Review on Performance Criteria and New-Generation Technologies
by Ali Mardanshahi, Abhilash Sreekumar, Xin Yang, Swarup Kumar Barman and Dimitrios Chronopoulos
Sensors 2025, 25(5), 1424; https://doi.org/10.3390/s25051424 - 26 Feb 2025
Cited by 7 | Viewed by 6375
Abstract
This systematic review examines the capabilities, challenges, and practical implementations of the most widely utilized and emerging sensing technologies in structural health monitoring (SHM) for infrastructures, addressing a critical research gap. While many existing reviews focus on individual methods, comprehensive cross-method comparisons have [...] Read more.
This systematic review examines the capabilities, challenges, and practical implementations of the most widely utilized and emerging sensing technologies in structural health monitoring (SHM) for infrastructures, addressing a critical research gap. While many existing reviews focus on individual methods, comprehensive cross-method comparisons have been limited due to the highly tailored nature of each technology. We address this by proposing a novel framework comprising five specific evaluation criteria—deployment suitability in SHM, hardware prerequisites, characteristics of the acquired signals, sensitivity metrics, and integration with Digital Twin environments—refined with subcriteria to ensure transparent and meaningful performance assessments. Applying this framework, we analyze both the advantages and constraints of established sensing technologies, including infrared thermography, electrochemical sensing, strain measurement, ultrasonic testing, visual inspection, vibration analysis, and acoustic emission. Our findings highlight critical trade-offs in scalability, environmental sensitivity, and diagnostic accuracy. Recognizing these challenges, we explore next-generation advancements such as self-sensing structures, unmanned aerial vehicle deployment, IoT-enabled data fusion, and enhanced Digital Twin simulations. These innovations aim to overcome existing limitations by enhancing real-time monitoring, data management, and remote accessibility. This review provides actionable insights for researchers and practitioners while identifying future research opportunities to advance scalable and adaptive SHM solutions for large-scale infrastructure. Full article
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20 pages, 3789 KiB  
Article
Explainable Intelligent Inspection of Solar Photovoltaic Systems with Deep Transfer Learning: Considering Warmer Weather Effects Using Aerial Radiometric Infrared Thermography
by Usamah Rashid Qureshi, Aiman Rashid, Nicola Altini, Vitoantonio Bevilacqua and Massimo La Scala
Electronics 2025, 14(4), 755; https://doi.org/10.3390/electronics14040755 - 14 Feb 2025
Cited by 2 | Viewed by 1135
Abstract
Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can [...] Read more.
Solar photovoltaic (SPV) arrays play a pivotal role in advancing clean and sustainable energy systems, with a worldwide total installed capacity of 1.6 terawatts and annual investments reaching USD 480 billion in 2023. However, climate disaster effects, particularly extremely hot weather events, can compromise the performance and resilience of SPV panels through thermal deterioration and degradation, which may lead to lessened operational life and potential failure. These heatwave-related consequences highlight the need for timely inspection and precise anomaly diagnosis of SPV panels to ensure optimal energy production. This case study focuses on intelligent remote inspection by employing aerial radiometric infrared thermography within a predictive maintenance framework to enhance diagnostic monitoring and early scrutiny capabilities for SPV power plant sites. The proposed methodology leverages pre-trained deep learning (DL) algorithms, enabling a deep transfer learning approach, to test the effectiveness of multiclass classification (or diagnosis) of various thermal anomalies of the SPV panel. This case study adopted a highly imbalanced 6-class thermographic radiometric dataset (floating-point temperature numerical values in degrees Celsius) for training and validating the pre-trained DL predictive classification models and comparing them with a customized convolutional neural network (CNN) ensembled model. The performance metrics demonstrate that among selected pre-trained DL models, the MobileNetV2 exhibits the highest F1 score (0.998) and accuracy (0.998), followed by InceptionV3 and VGG16, which recorded an F1 score of 0.997 and an accuracy of 0.998 in performing the smart inspection of 6-class thermal anomalies, whereas the customized CNN ensembled model achieved both a perfect F1 score (1.000) and accuracy (1.000). Furthermore, to create trust in the intelligent inspection system, we investigated the pre-trained DL predictive classification models using perceptive explainability to display the most discriminative data features, and mathematical-structure-based interpretability to portray multiclass feature clustering. Full article
(This article belongs to the Special Issue Power Electronics and Renewable Energy System)
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25 pages, 75362 KiB  
Article
Comprehensive Technical Inspection of a Medieval Bridge (Ponte de Vilanova, in Allariz) Using Microtechnological Tools
by Rubén Rodríguez Elizalde
Eng 2024, 5(4), 3259-3283; https://doi.org/10.3390/eng5040171 - 10 Dec 2024
Cited by 3 | Viewed by 1208
Abstract
Ponte de Vilanova, a masonry bridge, was built in Allariz, Galicia in the 13th–14th centuries. It is still standing. The structure, generally well preserved, shows minor deformations and wear signs caused by environmental factors. To conduct a comprehensive assessment without impacting the bridge’s [...] Read more.
Ponte de Vilanova, a masonry bridge, was built in Allariz, Galicia in the 13th–14th centuries. It is still standing. The structure, generally well preserved, shows minor deformations and wear signs caused by environmental factors. To conduct a comprehensive assessment without impacting the bridge’s integrity, drones equipped with thermal and underwater imaging technology were employed. Aerial inspections revealed vegetation growth and minor efflorescence (salt deposits) in some areas, while aerial thermography detected temperature variations along the stone joints, indicating the presence of moisture. The granite blocks comprising the bridge showed consistent quality and preservation. The underwater inspection confirmed that the bridge’s piers are well set on the riverbed, with no major damage observed, ruling out the immediate need for repair. This approach allowed a thorough evaluation of submerged parts without requiring divers, enhancing safety and reducing costs. Full article
(This article belongs to the Section Materials Engineering)
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40 pages, 79804 KiB  
Article
The GRAZ Method—Determination of Urban Surface Temperatures from Aerial Thermography Based on a Three-Dimensional Sampling Algorithm
by Daniel Rüdisser, Thomas Posch and Wolfgang Sulzer
Remote Sens. 2024, 16(21), 3949; https://doi.org/10.3390/rs16213949 - 23 Oct 2024
Viewed by 1545
Abstract
A novel method to derive surface temperatures from aerial thermography is proposed. Its theoretical foundation, details regarding the implementation, relevant sensitivities, and its application on a day and night survey are presented here. The method differs from existing approaches particularly in two aspects: [...] Read more.
A novel method to derive surface temperatures from aerial thermography is proposed. Its theoretical foundation, details regarding the implementation, relevant sensitivities, and its application on a day and night survey are presented here. The method differs from existing approaches particularly in two aspects: first, a three-dimensional sampling approach is used to determine the reflected thermal radiation component. Different surface classes based on hyperspectral classification with specific properties regarding the reflection and emission of thermal radiation are considered in this sampling process. Second, the method relies on a detailed, altitude-dependent, directionally and spectrally resolved modelling of the atmospheric radiation transfer and considers the spectral sensitivity of the sensor used. In order to accurately consider atmospheric influences, the atmosphere is modelled as a function of altitude regarding temperature, pressure and greenhouse gas concentrations. The atmospheric profiles are generated specifically for the time of the survey based on measurements, meteorological forecasts and generic models. The method was initially developed for application in urban contexts, as it is able to capture the pronounced three-dimensional character of such environments. However, due to the detailed consideration of elevation and atmospheric conditions, the method is also valuable for the analysis of rural areas. The included case studies covering two thermographic surveys of city area of Graz during daytime and nighttime demonstrate the capabilities and feasibility of the method. In relation to the detected brightness temperatures apparent to the sensor, the determined surface temperatures vary considerably and generally cover an increased temperature range. The two processed surface temperature maps of the city area of Graz are finally used to validate the method based on available temperature recordings. Full article
(This article belongs to the Section Urban Remote Sensing)
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14 pages, 5474 KiB  
Article
Assessment of Staining Patterns in Facades Using an Unmanned Aerial Vehicle (UAV) and Infrared Thermography
by João Arthur dos Santos Ferreira, Fernanda Ramos Luiz Carrilho, Jean Augusto Ortiz Alcantara, Camile Gonçalves, Carina Mariane Stolz, Mayara Amario and Assed N. Haddad
Drones 2024, 8(10), 542; https://doi.org/10.3390/drones8100542 - 1 Oct 2024
Cited by 2 | Viewed by 1352
Abstract
The emergence of pathological manifestations on facades persists globally, with recurring failures occurring often due to repeated construction details or design decisions. This study selected a building with a recurring architectural design and evaluated the stain pattern on its facade using a UAV [...] Read more.
The emergence of pathological manifestations on facades persists globally, with recurring failures occurring often due to repeated construction details or design decisions. This study selected a building with a recurring architectural design and evaluated the stain pattern on its facade using a UAV with an infrared thermal camera. The results showed that advanced technology offers a non-invasive and efficient approach for comprehensive inspections, enabling early detection and targeted interventions to preserve architectural assets without requiring ancillary infrastructure or risking workers at height. The precise identification of damage clarified the real causes of the observed pathological manifestations. Capturing the images allowed accurate inspection, revealing hollow and damp spots not visible to the human eye. Novel results highlight patterns in the appearance of dirt on facades, related to water flow that could have been redirected through proper geometric element execution. The presented inspection methodology, staining standards, and construction details can be easily applied to any building, regardless of location. Sills, drip pans, and flashings must have drip cuts, adequate inclination, and projections to prevent building degradation. Full article
(This article belongs to the Section Drone Design and Development)
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25 pages, 7437 KiB  
Article
Electrothermal Modeling of Photovoltaic Modules for the Detection of Hot-Spots Caused by Soiling
by Peter Winkel, Jakob Smretschnig, Stefan Wilbert, Marc Röger, Florian Sutter, Niklas Blum, José Antonio Carballo, Aránzazu Fernandez, Maria del Carmen Alonso-García, Jesus Polo and Robert Pitz-Paal
Energies 2024, 17(19), 4878; https://doi.org/10.3390/en17194878 - 28 Sep 2024
Cited by 1 | Viewed by 1646
Abstract
Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to [...] Read more.
Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article
(This article belongs to the Special Issue Advances in Photovoltaic Solar Energy II)
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22 pages, 5790 KiB  
Article
A Thermal Model for Rural Housing in Mexico: Towards the Construction of an Internal Temperature Assessment System Using Aerial Thermography
by Miguel Moctezuma-Sánchez, David Espinoza Gómez, Luis Bernardo López-Sosa, Iman Golpour, Mario Morales-Máximo and Ricardo González-Carabes
Buildings 2024, 14(10), 3075; https://doi.org/10.3390/buildings14103075 - 26 Sep 2024
Cited by 3 | Viewed by 1727
Abstract
Estimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial [...] Read more.
Estimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial thermography. The methodology used in this study considered three stages: (a) generating a semi-experimental thermal model of heat transfer through roofs for houses with high infiltration, (b) validating the model using contact thermometers in rural community houses, and (c) integrating the developed model using aerial thermography and Python 3.11.4 into user-friendly software. The results demonstrate that the thermal model is effective, as it was tested on two rural house configurations and achieved an error margin of less than 10% when predicting both maximum and minimum temperatures compared to actual measurements. The model consistently estimates the internal house temperatures using aerial thermography by measuring the roof temperatures. Experimental comparisons of internal temperatures in houses with concrete and asbestos roofs and the model’s projections showed deviations of less than 3 °C. The developed software for this purpose relies solely on the fundamental thermal properties of the roofing materials, along with the maximum roof temperature and ambient temperature, making it both efficient and user-friendly for rural community management systems. Additionally, the model identified areas with comfortable temperatures within different sections of a rural community, demonstrating its effectiveness when integrated with aerial thermography. These findings suggest the potential to estimate comfortable temperature ranges in both rural and urban dwellings, while also encouraging the development of public policies aimed at improving rural housing. Full article
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28 pages, 11761 KiB  
Article
Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring
by Usamah Rashid Qureshi, Aiman Rashid, Nicola Altini, Vitoantonio Bevilacqua and Massimo La Scala
Smart Cities 2024, 7(3), 1261-1288; https://doi.org/10.3390/smartcities7030053 - 28 May 2024
Cited by 11 | Viewed by 2672
Abstract
Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study [...] Read more.
Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering. Full article
(This article belongs to the Special Issue Smart Electronics, Energy, and IoT Infrastructures for Smart Cities)
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23 pages, 7536 KiB  
Article
Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks
by Eberechi Ichi and Sattar Dorafshan
Appl. Sci. 2024, 14(6), 2455; https://doi.org/10.3390/app14062455 - 14 Mar 2024
Cited by 7 | Viewed by 2000
Abstract
Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacement, can be autonomously and objectively detected using infrared thermography (IRT) [...] Read more.
Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacement, can be autonomously and objectively detected using infrared thermography (IRT) data with developed deep learning AI models to address some of the limitations associated with manned inspection. As one of the most promising classifiers, deep convolutional neural networks (DCNNs) have not been utilized to their fullest potential for delamination detection, arguably due to the scarcity of realistic ground truth datasets. In this study, a common encoder–decoder semantic segmentation-based DCNN is adapted through domain adaptation. The model was tuned and trained on a publicly available dataset to detect subsurface delamination in IRT data collected from in-service bridge decks. The authors investigated the effect of dataset augmentation, class imbalance, the number of classes, and the effect of background removal in the training dataset, resulting in an overall number of seventy-five UNET models. Four out of five bridges were adopted for training and validation, and the fifth bridge was for testing. Most models averaged 80 iterations, and the training progress finally reached a training accuracy of 75% with a loss of about 0.6 without any overfitting. The result showed a substantial difference in the minimum and maximum values for the evaluated performance metrics (0.447 and 0.773 for global accuracy, 0.494 and 0.657 for mean accuracy, 0.239 and 0.716 for precision, 0.243 and 0.558 for true positive rate (TPR), 0.529 and 0.899 for true negative rate (TNR), 0.282 and 0.550 for F1-score. The results also indicated that the models trained on the raw annotated balanced dataset performed best for half of the metrics. In contrast, the models trained on raw data (with no dataset enhancement) performed better when only global accuracy was considered. Full article
(This article belongs to the Special Issue Non-destructive Testing of Materials and Structures - Volume II)
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29 pages, 14878 KiB  
Article
A Feasibility Study of Thermal Infrared Imaging for Monitoring Natural Terrain—A Case Study in Hong Kong
by Lydia Sin-Yau Chiu, Wallace Wai-Lok Lai, Sónia Santos-Assunção, Sahib Singh Sandhu, Janet Fung-Chu Sham, Nelson Fat-Sang Chan, Jeffrey Chun-Fai Wong and Wai-Kin Leung
Remote Sens. 2023, 15(24), 5787; https://doi.org/10.3390/rs15245787 - 18 Dec 2023
Cited by 4 | Viewed by 2441
Abstract
The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey [...] Read more.
The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey via a UAV equipped with a thermal camera are limited. Given the unique setting of Hong Kong, which has a high population living in largely hilly terrain with little natural flat land, steep man-made slopes and natural hillsides have caused significant geotechnical problems which pose hazards to life and facilities. This paper presents the adoption of a time-lapse IRT survey using a UAV in such challenging geotechnical conditions. Snapshot and time-lapse IRT studies of a selected site in Hong Kong, where landslides had occurred were carried out, and visual inspection, photogrammetry, and IRT techniques were also conducted. 3D terrain models of the selected sites were created by using data collected from the photogrammetry and single (snapshot) and continuous monitoring (time-lapse) infrared imaging methods applied in this study. The results have successfully identified various thermal infrared signatures attributed to the existence of moisture patches, seepage, cracks/discontinuities, vegetation, and man-made structures. Open cracks/discontinuities, moisture, vegetation, and rock surfaces with staining can be identified in snapshot thermal image, while the gradient of temperature decay plotted in ln(T) vs. ln(t) enables quantifiable identifications of the above materials via time-lapse thermography and analysis. Full article
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8 pages, 5825 KiB  
Proceeding Paper
Diagnosis, Photogrammetry and Conservation Treatment with Nanomaterials of Sacidava Fortress
by Rodica-Mariana Ion, Lorena Iancu, Ramona Marina Grigorescu, Sorin Marcel Colesniuc, Verginica Schroder, Raluca Andreea Trandafir, Silviu Ionita, Anca Irina Gheboianu and Sofia Slamnoiu-Teodorescu
Chem. Proc. 2023, 13(1), 25; https://doi.org/10.3390/chemproc2023013025 - 23 Nov 2023
Cited by 1 | Viewed by 1503
Abstract
The diagnosis, thermography, aerial photogrammetry, and conservation treatment with nanomaterials (CHAp) for some samples from Sacidava Fortress, Romania, are analyzed and the results are discussed accordingly in this paper. Full article
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18 pages, 13591 KiB  
Article
Remotely Sensing the Invisible—Thermal and Magnetic Survey Data Integration for Landscape Archaeology
by Jegor K. Blochin, Elena A. Pavlovskaia, Timur R. Sadykov and Gino Caspari
Remote Sens. 2023, 15(20), 4992; https://doi.org/10.3390/rs15204992 - 17 Oct 2023
Cited by 4 | Viewed by 2348
Abstract
Archaeological landscapes can be obscured by environmental factors, rendering conventional visual interpretation of optical data problematic. The absence of evidence can lead to seemingly empty locations and isolated monuments. This, in turn, influences the cultural–historical interpretation of archaeological sites. Here, we assess the [...] Read more.
Archaeological landscapes can be obscured by environmental factors, rendering conventional visual interpretation of optical data problematic. The absence of evidence can lead to seemingly empty locations and isolated monuments. This, in turn, influences the cultural–historical interpretation of archaeological sites. Here, we assess the potential of integrating thermal and magnetic remote sensing methods in the detection and mapping of buried archaeological structures. The area of interest in an alluvial plain in Tuva Republic makes the application of standard methods like optical remote sensing and field walking impractical, as natural vegetation features effectively hide anthropogenic structures. We combined drone-based aerial thermography and airborne and ground-based magnetometry to establish an approach to reliably identifying stone structures concealed within alluvial soils. The data integration led to the discovery of nine buried archaeological structures in proximity to an Early Iron Age royal tomb, shedding light on ritual land use continuity patterns. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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19 pages, 16558 KiB  
Article
A Novel Approach for Efficient Solar Panel Fault Classification Using Coupled UDenseNet
by Radityo Fajar Pamungkas, Ida Bagus Krishna Yoga Utama and Yeong Min Jang
Sensors 2023, 23(10), 4918; https://doi.org/10.3390/s23104918 - 19 May 2023
Cited by 22 | Viewed by 3170
Abstract
Photovoltaic (PV) systems have immense potential to generate clean energy, and their adoption has grown significantly in recent years. A PV fault is a condition of a PV module that is unable to produce optimal power due to environmental factors, such as shading, [...] Read more.
Photovoltaic (PV) systems have immense potential to generate clean energy, and their adoption has grown significantly in recent years. A PV fault is a condition of a PV module that is unable to produce optimal power due to environmental factors, such as shading, hot spots, cracks, and other defects. The occurrence of faults in PV systems can present safety risks, shorten system lifespans, and result in waste. Therefore, this paper discusses the importance of accurately classifying faults in PV systems to maintain optimal operating efficiency, thereby increasing the financial return. Previous studies in this area have largely relied on deep learning models, such as transfer learning, with high computational requirements, which are limited by their inability to handle complex image features and unbalanced datasets. The proposed lightweight coupled UdenseNet model shows significant improvements for PV fault classification compared to previous studies, achieving an accuracy of 99.39%, 96.65%, and 95.72% for 2-class, 11-class, and 12-class output, respectively, while also demonstrating greater efficiency in terms of parameter counts, which is particularly important for real-time analysis of large-scale solar farms. Furthermore, geometric transformation and generative adversarial networks (GAN) image augmentation techniques improved the model’s performance on unbalanced datasets. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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