Abstract
Large-scale, cross-species plant distribution prediction plays a crucial role in biodiversity conservation, yet modeling efforts in this area still face significant challenges due to the sparsity and bias of observational data. Presence-Absence (PA) data provide accurate and noise-free labels, but are costly to obtain and limited in quantity; Presence-Only (PO) data, by contrast, offer broad spatial coverage and rich spatiotemporal distribution, but suffer from severe label noise in negative samples. To address these real-world constraints, this paper proposes a multimodal fusion framework that fully leverages the strengths of both PA and PO data. We introduce an innovative pseudo-label aggregation strategy for PO data based on the geographic coverage of satellite imagery, enabling geographic alignment between the label space and remote sensing feature space. In terms of model architecture, we adopt Swin Transformer Base as the backbone for satellite imagery, utilize the TabM network for tabular feature extraction, retain the Temporal Swin Transformer for time-series modeling, and employ a stackable serial tri-modal cross-attention mechanism to optimize the fusion of heterogeneous modalities. Furthermore, empirical analysis reveals significant geographic distribution shifts between PA training and test samples, and models trained by directly mixing PO and PA data tend to experience performance degradation due to label noise in PO data. To address this, we draw on the mixture-of-experts paradigm: test samples are partitioned according to their spatial proximity to PA samples, and different models trained on distinct datasets are used for inference and post-processing within each partition. Experiments on the GeoLifeCLEF 2025 dataset demonstrate that our approach achieves superior predictive performance in scenarios with limited PA coverage and pronounced distribution shifts.
Abstract (translated)
大规模、跨物种的植物分布预测在生物多样性保护中起着关键作用，但在这一领域建模工作仍面临观测数据稀疏和偏差等重大挑战。存在-不存在（PA）数据提供准确且无噪声的标签，但获取成本高昂且数量有限；仅存在（PO）数据则提供了广泛的空间覆盖范围以及丰富的时间空间分布信息，但却因负样本中严重的标签噪声而受到影响。 为了解决这些实际限制，本文提出了一种多模态融合框架，该框架充分利用了PA和PO数据各自的优点。我们引入了一种基于卫星图像地理覆盖的创新伪标签聚合策略，从而实现标签空间与遥感特征空间之间的地理位置对齐。在模型架构方面，我们采用Swin Transformer Base作为卫星图像的基础网络，并使用TabM网络进行表格特征提取，保留时间序列建模中的Temporal Swin Transformer，同时利用可堆叠串行三模式交叉注意机制来优化异构模式的融合。 此外，实证分析显示PA训练和测试样本之间存在显著的地理分布偏移，直接混合PO和PA数据训练模型往往会因为PO数据中标签噪声而导致性能下降。为了应对这一问题，我们借鉴了专家混合范式：根据其与PA样本的空间接近程度对测试样本进行划分，并在每个分区使用不同数据集上训练的不同模型来进行推理和后处理。 通过GeoLifeCLEF 2025数据集的实验表明，我们的方法在PA覆盖范围有限且分布偏移显著的情况下达到了优越的预测性能。
URL
https://arxiv.org/abs/2602.08282
