A large‐scale dataset of buildings and construction sites

Abstract

With the rapid development of deep learning and machine automation technology, as well as workforce aging, increasing labor costs, and other issues, an increasing number of scholars have paid attention to the use of these techniques to solve problems in civil engineering. Although progress has been made in applying deep learning to damage detection, many subfields in civil engineering are still in the initial stage, and a large amount of data has not been used. Moreover, the rapid development of a field cannot be separated from large open‐source datasets and many researchers. Therefore, this study attempts to construct a dataset named the BCS dataset of nearly 212,000 photos of buildings and construction sites using multi‐threaded parallel crawler technology and offline collection. The dataset will be expanded regularly. As a practical demonstration, the StyleGAN3 and StyleGAN2 generative adversarial networks were utilized on the dataset to create faked safety hat images and high‐resolution architectural images. Subsequently, four classic classification models were employed to validate the dataset, achieving a Top‐1 accuracy of up to 0.947. These results underscore the dataset's excellent potential for practical applications.