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<p>Recent advancements in computer vision and deep learning have advanced automated vegetation monitoring, creating new opportunities for invasive species management. To this end, we introduce MAVSD (Multi-Angle View Segmentation Dataset), specifically designed for detecting <i>Solidago canadensis L</i>., a globally significant invasive plant. The dataset comprises high-resolution images captured by unmanned aerial vehicles from four angles (30°, 45°, 60°, and 90°), providing comprehensive coverage of plant structures and enabling in-depth understanding from multiple perspectives. MAVSD includes pixel-level semantic segmentation annotations across 13 classes, meticulously categorizing vegetation and environmental elements. Extensive experiments with state-of-the-art segmentation models validate MAVSD’s effectiveness in enhancing invasive species detection and monitoring, with multi-angle training improving mIoU by up to 11% over single-angle baselines. The dataset’s multi-angle, high-resolution characteristics strengthen ecological monitoring capabilities, offering valuable resources for research and environmental protection applications.</p>

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MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

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  • Hanru Li,
  • Tianning Fu,
  • Hongchi Hao,
  • Zhibin Yu

摘要

Recent advancements in computer vision and deep learning have advanced automated vegetation monitoring, creating new opportunities for invasive species management. To this end, we introduce MAVSD (Multi-Angle View Segmentation Dataset), specifically designed for detecting Solidago canadensis L., a globally significant invasive plant. The dataset comprises high-resolution images captured by unmanned aerial vehicles from four angles (30°, 45°, 60°, and 90°), providing comprehensive coverage of plant structures and enabling in-depth understanding from multiple perspectives. MAVSD includes pixel-level semantic segmentation annotations across 13 classes, meticulously categorizing vegetation and environmental elements. Extensive experiments with state-of-the-art segmentation models validate MAVSD’s effectiveness in enhancing invasive species detection and monitoring, with multi-angle training improving mIoU by up to 11% over single-angle baselines. The dataset’s multi-angle, high-resolution characteristics strengthen ecological monitoring capabilities, offering valuable resources for research and environmental protection applications.