用AI无人机影像自动识别盐沼植被并绘制优势度图。
EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
- 基于Transformer与ConvNeXt的模块化图像处理流程
- 物种分割平均IoU达0.56，分类F1高达0.99
- 结果与实地调查高度一致，适合生态监测应用
通过低空无人机获取的高分辨率RGB影像，经由模块化处理流程：基于Transformer的语义分割、连通域植被提取、采用ConvNeXt架构的细粒度物种分类，以及2×2m网格的主导性评分，实现了对两种重要耐盐草本植物Spartina maritima和Puccinellia maritima的监测。模型在自建并人工标注的无人机影像及公开生物多样性数据集上训练。分割任务获得0.56的平均交并比（mean IoU）和0.96的像素级准确率；对象级分类实现0.99的F1分数。主导性估算与样方实地调查结果差异均值低于8%，在真实调查条件下保持了精细空间结构。该系统命名为EcoVision，为可扩展、高分辨率的盐沼监测提供了实用基础，展示了如何将像素级预测转化为生态可解释指标。
原文摘要 · Abstract (English)
High-resolution RGB imagery acquired from low-altitude UAV surveys was processed through a modular pipeline incorporating transformer-based semantic segmentation, connected-component vegetation extraction, fine-grained species classification using a ConvNeXt architecture, and grid-based dominance scoring at 2x2m resolution. The framework targeted two ecologically significant halophytic grasses, Spartina maritima and Puccinellia maritima, and was trained using a curated and manually annotated UAV imagery, along with biodiversity imagery sourced from publicly accessible datasets. In order to identify these plants from the imagery, our segmentation yielded reliable species masks (mean IoU = 0.56; pixel-level accuracy = 0.96), while object-level classification achieved very good discrimination (F1 = 0.99). Dominance estimates closely matched quadrat-based field surveys, with mean absolute differences below 8%, preserving fine-scale spatial structure under realistic survey conditions. The developed system, named EcoVision, establishes a practical foundation for scalable, high-resolution salt marsh monitoring, demonstrating how AI-driven workflows can translate pixel-level predictions into ecologically interpretable metrics.
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