Non-Protective Personal Equipment (PPE) detection is crucial on construction sites. Although deep learning models are adept at identifying such information from on-site cameras, their success relies on large, diverse, and high-quality datasets. Image augmentation offers an alternative for artificially broadening dataset diversity. However, its impact on non-PPE detection in construction environments has not been adequately examined. This study introduces a methodology applying eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation, and a combined strategy incorporating all methods. Model performance was assessed by comparing accuracy across different classes and architectures, both with and without augmentation. While most of these augmentations improved accuracy, their effectiveness was found to be task-dependent. Moreover, the most beneficial augmentation varied by non-PPE class and architecture, suggesting that augmentation strategies should be tailored to the unique features of each class and model. Although the primary focus here is on non-PPE, the evaluated techniques could also extend to related tasks on construction sites, such as detecting heavy equipment or identifying hazardous worker behavior.

Building characteristics are vital across various domains such as construction management and architectural design. Static Street View Images (SSVIs) can be utilized with deep learning techniques to interpret building characteristics without the need for a physical visit. Deep learning approaches have demonstrated a high capability for generalization, enabling the automation of manual tasks related to image analysis. However, there is no publicly available labeled dataset of building characteristics from building facade images for training deep learning models. In this article, we focus on constructing a dataset for four different tasks: classification of the number of stories, classification of building typologies, classification of exterior cladding materials, and classification of usable SSVIs. To develop deep learning models, this article constructed a dataset sourced from London and Scotland in the UK. The dataset was labeled by annotation experts. While the focus of this research is on specific tasks, the raw dataset can be used for other purposes (e.g., ascertaining the age of buildings or identifying window types) by annotating the data for the corresponding tasks.