## 6 Research Areas
### Knowledge Extraction  
Computer vision is used extensively to create a rich set of information from site images and videos for inspection purposes [216]. Computer vision and deep-learning have been used to detect defects in a building structure, such as exposed aggregates on a concrete surface [217] or cracks on the walls of storage tanks [218]. It is also used for post-disaster reconnaissance of damaged buildings [148; 151]. CNNs have performed remarkably well in detecting defects from images [219]. [PERSON] et al. [217] trained a U-net (a CNN-based deep-learning model) to identify exposed aggregate on a concrete surface. The U-net is a popular deep-learning model used for pixel-wise image segmentation. With a training dataset of 408 images and a validation dataset of 52 images, they achieved an accuracy of 91.43%. The results are promising, which could be further improved with a larger dataset. [PERSON] and [PERSON] [220] used a publicly available dataset of forty thousand images to train a deep-learning model to identify cracks on concrete and asphalt surfaces. They achieved an F1-score of up to 99.1%, which is a measure of how well a classifier function performs. The major challenge with crack detection using deep-learning is the wide range of complex backgrounds due to different types of facade materials [221].  
Computer vision has also been used for the automatic creation of as-built models from site images. [PERSON] et al. [169] used computer vision to identify components of indoor partitions, such as electrical outlets and doors using pictures captured from a UAV. [PERSON] et al. [52] further developed the technique to compare as-built elements with the as-designed elements in an IFC database. If a discrepancy is found, the element type, property, or geometry is updated to build an as-built model automatically.  
Researchers also developed non-ML-based image processing approaches that do not require big training datasets. For example, mathematical transformations for stitching multiple images across a single site to create a panoramic view of the project has been developed for inspection [179; 222; 223]. Photogrammetry, as explained in Section 5.4, is an image-processing technique [224]. Pix4D is a commercial platform used for photogrammetry that provides the option of taking building measurements from a 3D model created from 2D images. Pix4D has been used for knowledge extraction (taking measurements) using robots [30]. Edge detection techniques, which find discontinuities in the image, such as the Laplacian of Gaussian algorithm, are used to detect cracks on walls [225; 226] but do not require training data.  
Another important consideration in using robots in the field is that due to their efficiency and speed in capturing data, the high volume of data may soon create data overload which slows down learning from the data [227]. Thus, it is important to extract only useful data from the complete pool of data collected by the robot. In this direction, [PERSON] and [PERSON] [227] developed a method to rank and select images captured by a UAV based on the number of construction-related objects visible in the image using semantic segmentation.