The dissertation explores innovative computational methods for mapping and reusing vacant buildings, a critical step in advancing sustainability within the architecture, engineering, and construction (AEC) industry. With vacant structures contributing significantly to environmental challenges, this research addresses the pressing need for efficient tools to document and assess building components for reuse, which aligns with circular economy principles. The study introduces a robust pipeline for automated 3D mapping, leveraging LiDAR and RGB data captured via consumer-grade devices like the iPad Pro. The methodology combines advanced geometric processing and machine learning techniques to segment point clouds and generate simplified solid surface models for early-phase architectural analysis.

The proposed tool, ReUseExplorer, was developed as an open and flexible platform, facilitating interoperability across industry-standard software. Rigorous field studies, conducted in collaboration with Link Arkitektur and municipal stakeholders, validated its feasibility for diverse applications, including resource inventory creation, environmental impact assessments, and historical documentation. Challenges such as data drift, device limitations, and computational efficiency were addressed through iterative refinements. By providing architects and practitioners with accessible tools for building reuse, this research contributes to reducing carbon footprints in the built environment and aligns with emerging regulatory frameworks. Future directions include enhancing the precision of segmentation models and expanding their integration with industry workflows.