用多模态模型在50米分辨率下绘制欧洲生物多样性图谱
Mapping biodiversity at very-high resolution in Europe
- 融合遥感、气候与物种数据，用深度物种分布模型预测物种组成
- 生成50米级生物多样性指标图与生境分类图，覆盖整个欧洲
- 支持跨物种依赖建模，适合生态规划与环境监测人员使用
本文提出一种分层多模态流程，用于在欧洲实现高分辨率生物多样性制图，整合物种分布建模、生物多样性指标生成与生境分类。该流程首先利用深度-物种分布模型（deep-SDM）在50×50米分辨率下，基于遥感、气候时间序列和物种出现数据预测物种组成；随后使用Pl@ntBERT——一种专为物种到生境映射设计的Transformer语言模型，生成生物多样性指标图与生境分类图。该方法实现了大陆尺度的物种分布图、生物多样性指标图与生境图的精细生成，提供高精度生态洞察。相比传统方法，该框架可联合建模物种间依赖关系，采用异构存在-缺失数据进行偏差感知训练，并从多源遥感输入中实现大规模推理。
原文摘要 · Abstract (English)
This paper describes a cascading multimodal pipeline for high-resolution biodiversity mapping across Europe, integrating species distribution modeling, biodiversity indicators, and habitat classification. The proposed pipeline first predicts species compositions using a deep-SDM, a multimodal model trained on remote sensing, climate time series, and species occurrence data at 50x50m resolution. These predictions are then used to generate biodiversity indicator maps and classify habitats with Pl@ntBERT, a transformer-based LLM designed for species-to-habitat mapping. With this approach, continental-scale species distribution maps, biodiversity indicator maps, and habitat maps are produced, providing fine-grained ecological insights. Unlike traditional methods, this framework enables joint modeling of interspecies dependencies, bias-aware training with heterogeneous presence-absence data, and large-scale inference from multi-source remote sensing inputs.
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