用深度学习精准识别农田外树木类型，提升生态评估精度。
Mapping and Classification of Trees Outside Forests using Deep Learning
- 融合卷积与注意力机制的FT-UNetFormer模型表现最优。
- 平均交并比达0.74，对林地和线状植被识别效果好。
- 适合需要高精度植被分类的农业生态研究者使用。
农田外树木（TOF）在农业景观中对生物多样性保护、碳汇及微气候调节具有重要作用。然而，现有研究多将其视为单一类别或依赖固定规则阈值，限制了生态解释力与区域适应性。为此，本文基于德国四个农业区的高分辨率航拍影像，构建新数据集，评估深度学习在TOF四类分层（林地、斑块、线状、孤立树）中的表现。比较了六种语义分割架构（ABCNet、LSKNet、FT-UNetFormer、DC-Swin、BANet、U-Net），其中FT-UNetFormer性能最佳，平均交并比达0.74，平均F1分数为0.84，凸显空间上下文理解的重要性。模型对林地与线状结构识别效果良好，但在高边缘密度的斑块与孤立树类别面临挑战。泛化实验表明，需采用区域多样化的训练数据以支持大范围可靠制图。相关数据与代码已开源。
原文摘要 · Abstract (English)
Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microclimates. Yet, most studies have treated TOF as a single class or relied on rigid rule-based thresholds, limiting ecological interpretation and adaptability across regions. To address this, we evaluate deep learning for TOF classification using a newly generated dataset and high-resolution aerial imagery from four agricultural landscapes in Germany. Specifically, we compare convolutional neural networks (CNNs), vision transformers, and hybrid CNN-transformer models across six semantic segmentation architectures (ABCNet, LSKNet, FT-UNetFormer, DC-Swin, BANet, and U-Net) to map four categories of woody vegetation: Forest, Patch, Linear, and Tree, derived from previous studies and governmental products. Overall, the models achieved good classification accuracy across the four landscapes, with the FT-UNetFormer performing best (mean Intersection-over-Union 0.74; mean F1 score 0.84), underscoring the importance of spatial context understanding in TOF mapping and classification. Our results show good results for Forest and Linear class and reveal challenges particularly in classifying complex structures with high edge density, notably the Patch and Tree class. Our generalization experiments highlight the need for regionally diverse training data to ensure reliable large-scale mapping. The dataset and code are openly available at https://github.com/Moerizzy/TOFMapper
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