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

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

Thermal anomaly detection based on saliency analysis from multimodal imaging sources

Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy

Development of a non-destructive testing method for thermal assessment of a district heating network

This thesis presents the development of a non-destructive testing (NDT)method for thermal assessment of pre-insulated district heating (DH) pipes with high accuracy, in which the development process from literature review to its present stage is presented and discussed. Pre-insulated DH pipes have been in use for more than 40 years. The thermal performance of these pipes decreases over time as a result of thermal aging, which leads to higher heat\ua0 losses. Present methods are unable to assess these heat losses with a high accuracy.The main idea with the method is to perform a temporary shutdown of a selected part of a network for less than two hours, which enables temperature measurements during the cooling phase. Measured temperatures are then used for analyzing the thermal performance of the pipes. The accessibility for temperature measurements on the pipes depend on the conditions in field. Thus, the methodology for the development of this cooling method involves different measuring points during different conditions in field. This thesis covers three conducted field tests during maintenance, which allowed for temperature measurements on the service pipe, the casing pipe, and connected valves. Furthermore, the method utilizes the copper wire, which is already embedded in the polyurethane insulation for detection of water leakage, as a sensor for measuring the mean temperature at copper wire position along the pipe under assessment. This thesis presents the possibilities and uncertainties with the cooling method at its present stage. The method shows good potential to meet the aim as an NDT method with high accuracy, and to be a future tool for the network owners

Development of a non-destructive field testing method for thermal assessment of district heating pipes

The thermal state of a district heating (DH) network governs the heat losses. It is a parameter considered if a change is to be made in the network. Heat losses are costly and economic aspects are important when planning for the maintenance or replacement of DH pipes. In addition, knowledge of the DH network, and of the parts of the network that may contribute to high heat losses, is important for system control. Pre-insulated DH pipes have been in use for over 40 years. The thermal performance of these pipes decreases over time as a result of thermal aging, leading to higher heat losses. Present methods cannot assess these heat losses with high accuracy. This thesis proposes a developed non-destructive cooling method, the main purpose of which is to perform a temporary shutdown of a selected part of a network, and where temperature measurements are performed during the cooling phase.This thesis presents the development process and the final method to use for thermally assessing pre-insulated DH pipes with high accuracy. The main research questions of this work were linked to the accessibility and measurability of the buried pipe or its connected parts. The methodology for developing the method is based on laboratory tests, field tests with several measurement points, and mathematical models of DH pipes and connected valves.The work resulted in a method and a user guide that can be used by network owners to assess parts of a DH network. A method that by a shorter shutdown, in the range of a few hours, can be used to capture the temperature decline in a DH pipe. Results indicate that drainage valves, which are directly connected to the underlying DH network, were suitable measurement points where the temperature-decline phase of the DH pipe could be captured. The method allowed a prediction of the thermal conductivity of a buried DH pipe in operation with 2% deviation from the reference value

Methods for Large-Scale Monitoring of District Heating Systems Using Airborne Thermography

District heating is a common way of providing heat to buildings in urban areas. The heat is carried by hot water or steam and distributed in a network of pipes from a central powerplant. It is of great interest to minimize energy losses due to bad pipe insulation or leakages in such district heating networks. As the pipes generally are placed underground, it may be difficult to establish the presence and location of losses and leakages. Toward this end, this work presents methods for large-scale monitoring and detection of leakages by means of remote sensing using thermal cameras, so-called airborne thermography. The methods rely on the fact that underground losses in district heating systems lead to increased surface temperatures. The main contribution of this work is methods for automatic analysis of aerial thermal images to localize leaking district heating pipes. Results and experiences from large-scale leakage detection in several cities in Sweden and Norway are presented