Comparison of building thermography approaches using terrestrial and aerial thermographic images

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

Concepts and tools to improve the thermal energy performance of buildings and urban districts - diagnosis, assessment, improvement strategies and cost-benefit analyses

Retrofitting existing buildings to optimize their thermal energy performance is a key factor in achieving climate neutrality by 2045 in Germany. Analyzing buildings in their current condition is the first step toward preparing effective and efficient energy retrofit measures. A high-quality building analysis helps to evaluate whether a building or its components are suitable for retrofitting or replacement. Subsequently, appropriate combinations of retrofit measures that create financial and environmental synergies can be determined. This dissertation is a cumulative work based on nine papers on the thermal analysis of existing buildings. The focus of this work and related papers is on thermography with drones for building audits, intelligent processing of thermographic images to detect and assess thermal weaknesses, and building modeling approaches to evaluate thermal retrofit options. While individual buildings are usually the focus of retrofit planning, this dissertation also examines the role of buildings in the urban context, particularly on a district level. Multiple adjacent buildings offer numerous possibilities for further improving retrofits, such as the economies of scale for planning services and material procurement, neighborhood dynamics, and exchange of experiences between familiar building owners. This work reveals the opportunities and obstacles for panorama drone thermography for building audits. It shows that drones can contribute to a quick and structured data collection, particularly for large building stocks, and thus complement current approaches for district-scale analysis. However, the significant distance between the drone camera and building, which is necessary for automated flight routes, and varying recording angles limit the quantitative interpretability of thermographic images. Therefore, innovative approaches were developed to process image datasets generated using drones. A newly designed AI-based approach can automate the detection of thermal bridges on rooftops. Using generalizations about certain building classes as demonstrated by buildings from the 1950s and 1960s, a novel interpretation method for drone images is suggested. It enables decision-making regarding the need to retrofit thermal bridges of recorded buildings. A novel optimization model for German single-family houses was developed and applied in a case study to investigate the financial and ecological benefits of different thermal retrofit measures. The results showed that the retrofitting of building façades can significantly save energy. However, they also revealed that replacing the heating systems turns out to be more cost-effective for carbon dioxide savings. Small datasets, limited availability of technical equipment, and the need for simplified assumptions for building characteristics without any information were the main challenges of the approaches in this dissertation

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 \ub0C and 1.0 \ub0C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground

Investigating the Quality of UAV-Based Images for the Thermographic Analysis of Buildings

Thermography for building audits is commonly carried out by means of terrestrial recording processes with static cameras. The implementation of drones to automatically acquire images from various perspectives can speed up and facilitate the procedure but requires higher recording distances, utilizes changing recording angles and has to contend with the effects of movement during image capture. This study investigates the influence of different drone settings on the quality of thermographic images for building audits in comparison to ground-based acquisition. To this end, several buildings are photographically captured via unmanned aerial vehicle and classical terrestrial means to generate a dataset of 968 images in total. These are analyzed and compared according to five quality criteria that are explicitly chosen for this study to establish best-practice rules for thermal image acquisition. We discover that flight speeds of up to 5 m/s have no visible effects on the image quality. The combination of smaller distances (22 m above a building) and a 45° camera angle are found to allow for both the qualitative and quantitative analysis of rooftops as well as a qualitative screening of building façades. Greater distances of 42 m between camera and building may expedite the acquisition procedure for larger-scaled district coverage but cannot be relied upon for thermal analyses beyond qualitative studies

Deep learning approaches to building rooftop thermal bridge detection from aerial images

Thermal bridges are weak points of building envelopes that can lead to energy losses, collection of moisture, and formation of mould in the building fabric. To detect thermal bridges of large building stocks, drones with thermographic cameras can be used. As the manual analysis of comprehensive image datasets is very time-consuming, we investigate deep learning approaches for its automation. For this, we focus on thermal bridges on building rooftops recorded in panorama drone images from our updated dataset of Thermal Bridges on Building Rooftops (TBBRv2), containing 926 images with 6,927 annotations. The images include RGB, thermal, and height information. We compare state-of-the-art models with and without pretraining from five different neural network architectures: MaskRCNN R50, Swin-T transformer, TridentNet, FSAF, and a MaskRCNN R18 baseline. We find promising results, especially for pretrained models, scoring an Average Recall above 50% for detecting large thermal bridges with a pretrained Swin-T Transformer model

A geomatic application for water management in precision farming for a LIFE European project

The activities and the approaches described in this work are carried out in the frame of the current European LIFE project AGROWETLANDS II (Principal Investigator: M. Speranza, University of Bologna). This multidisciplinary project aims at the integration of multi-source data, to provide a valuable support for a more efficient and sustainable management of territorial resources, through an innovative approach to precision farming. The implementation of a smart irrigation management system is a central task of AGROWETLANDS II. A pilot system is operational in a study area located close to the Northern coast of the Adriatic Sea, in the Ravenna province (Italy). It is a farming area affected by soil and groundwater salinization, due to the ingression of sea water. The permanent monitoring network records several parameters for the weather, soil and groundwater. The expected results of the project include: a mitigation in the soil salinity, an improvement in the productivity of the crops, a reduction of the environmental impact and the implementation of a decision support system (DSS), which is able to advise farmers on the management of their agricultural activities (LIFE AGROWETLANDS II, 2019). In fact, one of the main goals of the project is to reduce the water use for crop production and to ensure a sustainable water management for an efficient agricultural production. In order to achieve significant results in precision farming and water management, an accurate survey is needed to create precise numerical models which are the necessary base for each subsequent analysis

Enemies with benefits: parasitic endoliths protect mussels against heat stress

Positive and negative aspects of species interactions can be context dependant and strongly affected by environmental conditions. We tested the hypothesis that, during periods of intense heat stress, parasitic phototrophic endoliths that fatally degrade mollusc shells can benefit their mussel hosts. Endolithic infestation significantly reduced body temperatures of sun-exposed mussels and, during unusually extreme heat stress, parasitised individuals suffered lower mortality rates than nonparasitised hosts. This beneficial effect was related to the white discolouration caused by the excavation activity of endoliths. Under climate warming, species relationships may be drastically realigned and conditional benefits of phototrophic endolithic parasites may become more important than the costs of infestation

Energy performance analysis between two air conditioning systems used in an educational Building in warm-climate

Energy saving measures, in the design air conditioning systems, are crucial in the development of energy schemes with rational energy consumption. Traditionally, integrated buildings systems have been assessed individually to optimize the energy performance, however they have different parameters that affect energy performance that demands the use of detailed analysis using dynamic simulation. This paper is focused on compare an air conditioning system to be implemented in educational buildings in warm-climate, considering energy schemes provide for a constant air volume (CAV) flow system with a water chiller, while the other integrates a variable refrigerant flow (VRF) system. Adding in each case dedicated outdoor air System (DOAS) units. Energy consumption achieved by each AC system is obtained considering the configuration achieving energy savings of 40% of the annual electricity demand for cooling. Finally, the use of DOAS represents an increase of 20% of total electricity consumption

Exergy-based Planning and Thermography-based Monitoring for energy efficient buildings - Progress Report (KIT Scientific Reports ; 7632)

Designing and monitoring energy efficiency of buildings is vital since they account for up to 40% of end-use energy. In this study, exergy analysis is investigated as a life cycle design tool to strike a balance between thermodynamic efficiency of energy conversion and economic and environmental costs of construction. Quantitative geo-referenced thermography is proposed for monitoring and quantitative assessment via continued simulation and parameter estimation during the operating phase