Coastal environments are vital for ecological stability, human activity, and climate resilience, yet they are increasingly affected by anthropogenic activities. Particularly, construction machinery such as bulldozers plays a critical role in altering the beach environment through beach nourishment purposes and coastal engineering, but their presence and movement are rarely tracked systematically. This research addresses that gap by developing a method to automatically identify bulldozers and other large dynamic objects from multiple epochs of permanent terrestrial laser scanning (TLS) point cloud data using multidimensional feature analysis and supervised machine learning.

This study presents a robust framework for automatically classifying bulldozers and other large dynamic objects from terrestrial laser scanning (TLS) point clouds, achieving a test accuracy of 92.5% with a k-Nearest Neighbours (k-NN) classifier. This framework integrates both 3D point cloud descriptors with 2D projection-based features. Starting from raw TLS data, object clusters are extracted and described using geometric features. For each object, 3D features including linearity, planarity, and verticality are computed; 2D raster-based descriptors, including footprint spread and height variation, are computed from XY and XZ plane projections. These features are aggregated to build an object-level dataset. At the same time, global descriptors of the horizontal and vertical extents are computed to capture the dimension information. Together, these features are then standardised at the object level to train supervised classifiers capable of distinguishing four object classes: 'large bulldozer', 'other bulldozer', 'tractor-trailer', and 'other'.

The evaluation reveals that the instance-based k-NN model consistently outperformed a Support Vector Machine (SVM), which proves less robust to class imbalance and dataset shift due to its reliance on a fixed global decision boundary. Feature importance analysis confirms that a combination of 3D descriptors capturing structural complexity (e.g., eigenentropy, omnivariance) and 2D projection features quantifying vertical profiles is the most discriminative. The framework's real-world applicability is validated on an independently and automatically segmented dataset, where the k-NN model maintains a high overall accuracy of 90.9%. This validation also highlights the classification performance's sensitivity to segmentation quality, as incomplete object data from partial occlusion predictably decreases accuracy

In conclusion, this study establishes a practical and reliable feature-based methodology for monitoring anthropogenic activity in dynamic coastal zones. Future work should focus on enhancing this framework by expanding the training dataset to improve robustness, implementing adaptive binning for 2D feature extraction to better handle scale variance, and integrating more advanced segmentation algorithms to enable a fully automated monitoring pipeline. Such improvements will further solidify the method's utility for long-term environmental monitoring and data-driven coastal management.