ArticleScientific data2025
MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Patterns, Associated Factors and Plant Diversity Characteristics ofPlants (Basel, Switzerland) · 2026Article
- An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation.Scientific data · 2025Article
- MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.Scientific data · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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