CUBia

The goal of CUBia is to develop and validate a precision forest timber cruising system that combines airborne (UAV) and terrestrial SLAM LiDAR with artificial intelligence models to estimate the weight, volume and number of trees of standing timber for Galicia’s main commercial species.

The project proposes a shift from manual field inventories to a decentralised, remote and digital model. Its key differentiator is that predictions are validated against timber actually weighed at the mill, ensuring the practical relevance of the models. Results will be integrated into a digital forest management platform, improving transparency and traceability across the forest value chain.

CUBia also opens an exploratory line in quantum machine learning (QML). ITG leads this line, designing and testing hybrid quantum-classical prototypes (QNN, QCNN) on real project data, and benchmarking them against classical and deep learning approaches.

Objectives:

• Develop predictive models of weight, volume and number of trees from high-density LiDAR point clouds, validated with industrial weighing data.
• Establish standardised capture and processing protocols combining UAV-LiDAR and SLAM LiDAR.
• Build a continuous, replicable learning system (MLOps) scalable across Galicia.
• Assess the potential of quantum and quantum-inspired techniques  for forest prediction and define a roadmap for their future integration.