AI and ML applications on plunging cliffs

This abstract examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in the geomorphological surveying of plunging cliffs, which represent some of the most dynamic and hazardous landforms on Earth. These steep and inaccessible environments are shaped by complex interactions between marine erosion, tectonics, weathering, and gravitational processes. Due to the complex logistics, traditional field-based surveying can be both risky and limited in spatial coverage. AI and ML techniques provide powerful tools to overcome these constraints by enabling the automated analysis of large, multi-source geospatial datasets, such as images, physical-chemical data, etc.

Data collected via swim surveys, drones, satellite imagery, and photogrammetry can be integrated into AI-driven workflows. Convolutional neural networks and other deep learning architectures can automatically detect coastal landforms, allowing detailed mapping of geomorphological features at unprecedented scales. Change detection algorithms applied to time-series datasets identify subtle deformation, rockfall precursors, and erosion patterns that may not be visible in the field or through manual interpretation. In parallel, ML-based classification and clustering methods help differentiate cliff characteristics, such as lithological units and surface conditions, improving the understanding of cliff geomorphic behaviour.

Overall, AI and ML can guide the transformation of plunging cliff geomorphological surveying from a largely manual and episodic practice into a continuous, high-resolution and, in the near future, predictive science. This approach not only enhances scientific insight into sea cliff dynamics but also provides practical tools for early warning systems, land-use planning, and the long-term management of these environments.