用AI自动识别河流侵蚀区域并计算面积，提升监测效率。
Detección y Cuantificación de Erosión Fluvial con Visión Artificial
- 用YOLOv11模型结合照片与激光雷达图训练，实现自动检测。
- 侵蚀区域识别准确率达70%，能精确计算像素和平方米面积。
- 开发了交互式网页工具EROSCAN，方便非专业人员使用。
河流侵蚀是影响土壤稳定与关键基础设施的自然过程。传统检测依赖摄影测量与地理信息系统分析，需专业知识且人工处理繁琐。本文提出基于人工智能的自动识别与量化方法，采用经微调的YOLOv11模型，使用照片与LiDAR图像联合训练。数据集通过Roboflow平台进行分割与标注。实验表明，该方法可高效识别侵蚀模式，准确率达70%，能精确识别侵蚀区域并可靠计算其面积（以像素和平方米为单位）。最终开发出EROSCAN系统——一个交互式网页应用，用户上传图像即可获得自动分割结果及面积估算。该工具显著优化了侵蚀现象的检测与量化，助力风险管理和国土规划决策。
原文摘要 · Abstract (English)
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.
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