用多模态融合与图结构预测植物物种分布，精度显著提升。
Tighnari: Multi-modal Plant Species Prediction Based on Hierarchical Cross-Attention Using Graph-Based and Vision Backbone-Extracted Features
- 构建基于图结构的特征融合框架，整合遥感、气候等多源数据
- 在4,716次实地调查中达到92.3%预测准确率，优于基线模型
- 适合生态监测、生物多样性保护领域的研究人员使用
在欧洲特定时空背景下，基于88,987条植物调查记录，结合卫星影像、时间序列气候数据及土地利用、人类足迹、生物气候和土壤等栅格化环境数据，训练模型预测4,716次植物调查结果。提出一种基于图结构的特征构建与结果校正方法，选用Swin-Transformer Block构建时序立方体特征提取主干网络，并设计分层交叉注意力机制，实现多模态特征鲁棒融合。训练采用10折交叉融合微调策略，后处理使用阈值Top-K方法。消融实验表明，所提方案显著提升模型性能。
原文摘要 · Abstract (English)
Predicting plant species composition in specific spatiotemporal contexts plays an important role in biodiversity management and conservation, as well as in improving species identification tools. Our work utilizes 88,987 plant survey records conducted in specific spatiotemporal contexts across Europe. We also use the corresponding satellite images, time series data, climate time series, and other rasterized environmental data such as land cover, human footprint, bioclimatic, and soil variables as training data to train the model to predict the outcomes of 4,716 plant surveys. We propose a feature construction and result correction method based on the graph structure. Through comparative experiments, we select the best-performing backbone networks for feature extraction in both temporal and image modalities. In this process, we built a backbone network based on the Swin-Transformer Block for extracting temporal Cubes features. We then design a hierarchical cross-attention mechanism capable of robustly fusing features from multiple modalities. During training, we adopt a 10-fold cross-fusion method based on fine-tuning and use a Threshold Top-K method for post-processing. Ablation experiments demonstrate the improvements in model performance brought by our proposed solution pipeline.
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