Detección y Cuantificación de Erosión Fluvial con Visión Artificial
This addresses the problem of labor-intensive manual monitoring of fluvial erosion for risk management and territorial planning, though it is incremental as it applies an existing computer vision model to a new domain.
The study tackled automatic detection and area estimation of fluvial erosion zones using an AI-based approach, achieving 70% accuracy in detecting erosion patterns and providing reliable area calculations in pixels and square meters.
Fluvial erosion is a natural process that can generate significant impacts on soil stability and strategic infrastructures. The detection and monitoring of this phenomenon is traditionally addressed by photogrammetric methods and analysis in geographic information systems. These tasks require specific knowledge and intensive manual processing. This study proposes an artificial intelligence-based approach for automatic identification of eroded zones and estimation of their area. The state-of-the-art computer vision model YOLOv11, adjusted by fine-tuning and trained with photographs and LiDAR images, is used. This combined dataset was segmented and labeled using the Roboflow platform. Experimental results indicate efficient detection of erosion patterns with an accuracy of 70%, precise identification of eroded areas and reliable calculation of their extent in pixels and square meters. As a final product, the EROSCAN system has been developed, an interactive web application that allows users to upload images and obtain automatic segmentations of fluvial erosion, together with the estimated area. This tool optimizes the detection and quantification of the phenomenon, facilitating decision making in risk management and territorial planning.