Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Hou, Yu; Volk, Rebekka; Chen, Meida; Soibelman, Lúcio

doi:10.1016/j.autcon.2021.103554

Cited by 30 publications

(9 citation statements)

References 58 publications

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“…In the first step of the procedure, it is necessary to record thermographic images with an UAV. For this, an UAV can be programmed to pursue an automatic flight route (e.g., in a mesh grid or a Y-grid) to cover a whole city district from all four cardinal directions [30].…”

Section: Methodsmentioning

confidence: 99%

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Aerial Thermographic Image-Based Assessment of Thermal Bridges Using Representative Classifications and Calculations

et al. 2021

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Since the middle of the 20th century many any buildings were built without any energy standards and still have a comparably poor energy quality. To obtain an overview of the current thermal quality of buildings in a whole city district, it may be promising to work with thermographic images obtained by unmanned aerial vehicles (UAV). Aerial thermography represents a fast and cost-efficient approach compared to traditional terrestrial thermography. In this paper, we describe an approach to finding thermal bridges on aerial thermographic images and characterizing them in terms of their risk of mold formation, energy losses, retrofit costs, and retrofit benefits. To identify thermal bridge types that can be detected reliably on aerial thermographic images, we use a dataset collected with a UAV in an urban district of the German city of Karlsruhe. We classify and characterize 14 relevant thermal bridge types for the German building cohorts of the 1950s and 1960s. Concerning the criterion of mold formation, thermal bridges of window components, basement ceiling slabs, balcony slabs, floor slabs, and attics are found to be particularly relevant to retrofit projects. Regarding energy savings, the retrofit of thermal bridges of window sills, window lintels, and attics shows high potential. The retrofit of attics seems to be less attractive, when also taking into account the necessary retrofit costs.

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Section: Methodsmentioning

confidence: 99%

“…They worked with building close-up thermography and tested their approach for a single school building. Hou et al [30] experimented with different flight patterns and angles for drones with thermographic cameras to record whole city districts as a basis for the analysis of the thermal quality of buildings.…”

Section: Introductionmentioning

confidence: 99%

Aerial Thermographic Image-Based Assessment of Thermal Bridges Using Representative Classifications and Calculations

et al. 2021

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“…RGB-Thermal Fusion: Thermal data are another data type that can be used to improve semantic segmentation [63]. For example, Laguela et al [64] researched how to generate a high-quality thermographic image of a building envelope by fusing infrared data with RGB images.…”

Section: Rgb-depth Fusionmentioning

confidence: 99%

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

et al. 2021

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As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.

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“…It becomes possible to obtain images at different angles (not just eye-level) and of high buildings that remain inaccessible via terrestrial thermography. Moreover, the thusly acquired images can easily be used to generate 3D models to provide a good overview of a whole district [13].…”

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confidence: 99%

“…They highlight the benefit of drone thermography for building energy audits, particularly of otherwise inaccessible building parts like rooftops [16]. Most recently, Hou et al [13] explore an approach for thermographic 3D model development of entire city districts using UAVs. Different flight patterns such as vertical grid, horizontal grid, and mesh grid, as well as 90° (nadir) and 45° viewing angles in combination with a high image overlap are analyzed.…”

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Comparison of building thermography approaches using terrestrial and aerial thermographic images

Mayer

Epperlein

Volk

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Thermography is commonly used for auditing buildings. Classical manual terrestrial thermography records images of individual buildings at a short distance. When auditing a large number of buildings (e.g. whole city districts) this approach reaches its limits. Using drones with thermographic cameras allows images to be recorded automatically from different angles, with faster speed and without violating property rights. However, an airborne camera has a significantly greater distance and more varied angles to a building compared to terrestrial thermography. To investigate the influence of these factors for building auditing, we perform a study evaluating seven different drone settings of varying flight speed, angle, and altitude. A comparison is drawn to manually recorded terrestrial thermographic images. While we find that a flight speed between 1m/s and 3m/s does not influence the thermographic quality, high flight altitudes and steep viewing angles lead to a significant reduction of visible details, contrast, and to falsified temperatures. A flight altitude of 12m over buildings is found to be the most suitable for the qualitative and quantitative analysis of rooftops and a qualitative analysis of façades. A flight altitude of 42m over buildings can only be used for qualitative audits with little detail.

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Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Cited by 30 publications

References 58 publications

Aerial Thermographic Image-Based Assessment of Thermal Bridges Using Representative Classifications and Calculations

Aerial Thermographic Image-Based Assessment of Thermal Bridges Using Representative Classifications and Calculations

An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

Comparison of building thermography approaches using terrestrial and aerial thermographic images

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