Recognizing as-built building materials: a systematic mapping study
Year: 2026
Type: article
Study design: Systematic Review
Abstract: • First systematic mapping study focused on as-built building material recognition. • Several data modalities and formats are applicable to recognizing building materials. • Multi-modal data with foundation models is an emerging approach. • A collection of multi-modal material recognition public datasets has been identified. Recognising materials in existing, as-built buildings is essential for analysing safety, energy performance, material reclamation, amongst many other use cases. As-built building material recognition is challenging because building materials often mimic others, and controlled laboratory conditions are not practical. This systematic mapping study is the first to focus on as-built building material recognition, consolidating fragmented research across disciplines to reveal data modalities, recognition techniques, research gaps, and key future directions. After reviewing over 20,000 documents, several key insights were identified: 1) many studies lack contextual information necessary to assess generalization; 2) visible light, hyperspectral, infrared, radiowave, tactile, audio, electric field, and text are potential data modalities; 3) studies are needed beyond the neighborhood scale; 4) experiments optimized for ease-of-use pair images with foundation models; 5) highly cited experiments use multi-modal data with foundation models. Additionally, a structured overview of public datasets is provided. This study supports researchers by establishing effective and scalable methods for as-built building material recognition applicable in many use cases.
Source: Expert Systems with Applications
Authors: Josie Harrison, Yinan Yu, Alexander Hollberg
Institution: Chalmers University of Technology
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W7154623720