Abstract
In recent decades, the construction industry has made significant steps towards digitalisation. One of the most persistent challenges remains ensuring data accuracy, particularly in building component classification. Recent research has explored the application of machine learning algorithms for element classification within Building Information Modelling (BIM). These methods, while effective in handling individual geometric features, often overlook the critical influence of an element’s spatial context, leading to misclassifications in dense and interconnected models. To address this gap, ContextNet proposes an innovative deep-learning approach that integrates contextual information from surrounding components, enhancing classification accuracy by accounting for spatial relationships. By processing point clouds from both individual elements and their context, ContextNet enables more robust identification of building components within BIM models. Results demonstrate that ContextNet significantly improves classification accuracy and robustness, reducing common misclassifications in complex BIM datasets. The proposed framework enhances BIM data by enabling contextaware component recognition, supporting more accurate and dependable classification within the construction industry.