Building envelope modeling calibration using aerial thermography

Bayomi, Norhan; Nagpal, Shreshth; Rakha, Tarek; Fernández, John

doi:10.1016/j.enbuild.2020.110648

Cited by 45 publications

(6 citation statements)

References 32 publications

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“…While it is also important to note that the XT-R and XT2 are no longer manufactured, they, continue to be very much in use [9,11,13,17,18,[21][22][23][24][26][27][28]68,69] and are sure to be primary instruments in new works to come. The M2EA is relatively new and highly capable, being able to compete (results-wise) with the XT2 system.…”

Section: Discussionmentioning

confidence: 99%

“…Within less than half a decade, capable small RPAS TIRI systems have been employed in a wide variety of studies, such as forestry [5][6][7], wildlife surveys [8][9][10][11], natural hazards [12][13][14], urban environments [15][16][17][18], archeology [19][20][21], mining [22][23][24], building inspection [25][26][27], etc. These works, as well as many others, have aided the overall technology push toward the use of thermography with small RPAS.…”

Section: Introductionmentioning

confidence: 99%

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A Practical Validation of Uncooled Thermal Imagers for Small RPAS

Leblanc

Kalácska

Arroyo‐Mora

et al. 2021

Drones

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Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R2 > 0.99) in the temperature range 5–40 °C as well as a small (<2 °C) temperature bias that is less than the stated accuracy of the cameras. Field validation was accomplished by imaging vegetation and concrete targets (outdoors and at night), that were instrumented with surface temperature sensors. Environmental parameters (air temperature, humidity, pressure and, wind and gusting) were measured for several hours prior to imaging data collection and found to either not be a factor, or were constant, during the ~30 min data collection period. In-field results from imagery at five heights between 10 m and 50 m show absolute temperature retrievals of the concrete and two vegetation sites were within the specifications of the cameras. The methodology has been developed with consideration of active RPAS operational requirements.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Practical Validation of Uncooled Thermal Imagers for Small RPAS

Leblanc

Kalácska

Arroyo‐Mora

et al. 2021

Drones

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“…Aerial thermal imaging, utilizing uncooled thermal cameras, suffers from vignetting nonuniformity, necessitating temperature calibration for accuracy [7]. Moreover, aerial thermography aids in calibrating building envelope models to meet energy efficiency standards [8][9].…”

Section: Fig 2 -Flat Roof Leak Samplementioning

confidence: 99%

Flat roof classification and leaks detections by Deep Learning

Zahradník,

Roučka,

Karlovská

2024

CEJ

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This paper presents an efficient and accurate method for detecting flat roof leaks using a combination of unmanned aerial vehicles (UAVs) and deep learning. The proposed method utilizes a DJI M300 drone equipped with RGB and thermal cameras to capture high-resolution images of the roof. These images are then processed to create orthomosaics and digital elevation models (DEMs). A deep learning model based on the U-NET architecture is then used to segment the roof into different classes, such as PVC foil, windows, and sidewalks. Finally, the damaged insulation is identified by analyzing the temperature distribution within the PVC foil segments. The proposed method has several advantages over traditional inspection methods. It is much faster and more efficient. A UAV can collect images of a large roof in a matter of minutes, while traditional methods can take several days or weeks. The orthomosaics and temperature maps generated by the UAV are much more detailed than the images that can be collected by a human inspector. Third, the UAV-based system is safer. The UAV can collect images of the roof without the need for a human inspector to climb onto the roof, which can be dangerous. The results of this study show that the proposed method is an effective and accurate way to detect flat roof leaks. The deep learning model was able to achieve an overall accuracy of 95% in segmenting the roof into different classes. The method was also able to identify damaged insulation with a high degree of accuracy.

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“…One major application is using thermographic cameras mounted on drones to detect heat leaks, air infiltration, exfiltration, and insulation deficiencies in roofs and facades. UAVs can be easily deployed to survey every side of a building's structure from optimal proximity compared to the ground-based thermography providing more accurate insights into the thermal performance of building envelopes [155]. A study by Rathinam et al [156] presented a comprehensive exploration of the utilization of UAVs for real-time structure detection and tracking, focusing on linear structures, such as roads, highways, and canals, based on the visual feedback.…”

Section: B Building Envelope Performancementioning

confidence: 99%

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

Bayomi,

Fernandez

2023

Drones

Self Cite

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This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities.

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Building envelope modeling calibration using aerial thermography

Cited by 45 publications

References 32 publications

A Practical Validation of Uncooled Thermal Imagers for Small RPAS

A Practical Validation of Uncooled Thermal Imagers for Small RPAS

Flat roof classification and leaks detections by Deep Learning

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

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