Efficient extraction of dimension stone blocks relies on understanding the structural discontinuities within the rock mass. Accurate characterisation of fractures, faults, and joints allows optimising extraction, minimising losses, and maximising natural resources utilisation. This study introduces a semi-automated approach for detecting and analysing discontinuity families based on structural data, 3D modelling, and field validation. A case study conducted in a dimension stone quarry demonstrated that the proposed method effectively characterised fractures and their influence on block segmentation. Three major discontinuity families with dip angles ranging from 60° to 85° were identified, and their orientation significantly influenced block geometry and extraction efficiency. Fractures presenting steeper dip angles (285°) aligned with natural structural planes, facilitating separation, while those with lower dip angles (≤60°) led to erratic block shapes, complicating extraction strategies. A progressive removal model of extracted rock slabs was also developed to dynamically visualise the quarry's future evolution. The proposed solution is applicable and scalable, enhancing extraction efficiency and optimising block recovery while integrating geospatial data analysis and structural geology. This approach contributes to mine planning and operational decision-making, highlighting the potential of combining advanced analysis tools with geological insights to modernise the dimension stone sector.