Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

Large datasets are necessary for deep learning as the performance of the algorithms used
increases as the size of the dataset increases. Poor data management practices and the low …

Many vision‐based indoor localization methods require tedious and comprehensive pre‐
mapping of built environments. This research proposes a mapping‐free approach to …

Infrastructure scene understanding from image data aids diverse applications in construction
and maintenance. Recently, deep learning models have been employed to extract …

Deep learning-based camera pose regression approaches have achieved outstanding
performance for visual indoor localisation. However, these approaches are limited by the …

Recently, deep neural networks have achieved remarkable performance in single-image
localisation, where the location and orientation of the camera is estimated using an …

Deep learning recognition models have been widely studied to recognize construction
objects from site images. These methods require high volumes of quality data from human …

Data collection, particularly ground-truth generation, is crucial for developing computer
vision models used for construction progress monitoring applications. The performance of …

In an era characterized by the rapid evolution of data-driven applications, the generation of
high-quality synthetic data has become increasingly indispensable. This serves as a crucial …

Accurate estimation of the position of pedestrians and mobile robots in large public buildings
is important for a 23 variety of applications including navigation, emergency response, and …