标题： 大陆尺度的人工智能与多模态地球观测生境建模 显示英文标题

标题： Continental scale habitat modelling with artificial intelligence and multimodal earth observation

摘要： 生境整合了支持生物多样性和维持自然对人类贡献的非生物条件和生物物理结构。 随着这些生态系统面临来自人类活动的日益增长的压力，准确且高分辨率的生境图对于有效的保护和恢复至关重要。 然而，当前的地图在主题或空间分辨率上常常不足，因为它们必须（1）建模几种在景观中共同出现的相互排斥的生境类型，以及（2）处理严重的类别不平衡，这使得多类训练变得复杂。 在此，我们评估了高分辨率遥感（RS）数据和人工智能（AI）工具如何在大地理范围内以精细的主题分辨率改进生境分类。 使用欧洲植被档案中的植被样方，我们在欧洲建模了EUNIS三级生境，并利用独立验证数据集评估了多种建模策略。 利用生境命名体系层次结构的策略解决了分类歧义，特别是在破碎化的景观中。 整合多光谱（MSI）和合成孔径雷达（SAR）图像，特别是通过地球观测基金会模型，提高了形成内的区分能力和整体性能。 最后，纠正类别不平衡的集成机器学习进一步提高了准确性。 我们的方法框架不仅适用于欧洲，还可适应其他分类系统。 未来的研究应推进动态生境的时间建模，扩展到生境分割和质量评估，并利用新一代地球观测数据与更高品质的地面观测数据相结合。 显示英文摘要

摘要： Habitats integrate the abiotic conditions and biophysical structures that support biodiversity and sustain nature's contributions to people. As these ecosystems face mounting pressure from human activities, accurate, high-resolution habitat maps are essential for effective conservation and restoration. Yet current maps often fall short in thematic or spatial resolution because they must (1) model several mutually exclusive habitat types that co-occur across landscapes and (2) cope with severe class imbalance that complicate multi-class training. Here, we evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat classification over large geographic extents at fine thematic resolution. Using vegetation plots from the European Vegetation Archive, we modelled Level 3 EUNIS habitats across Europe and assessed multiple modelling strategies against independent validation datasets. Strategies that exploited the hierarchical nature of habitat nomenclatures resolved classification ambiguities, especially in fragmented landscapes. Integrating multi-spectral (MSI) and synthetic aperture radar (SAR) imagery, particularly through Earth Observation Foundation models, enhanced within-formation discrimination and overall performance. Finally, ensemble machine learning that corrects class imbalance boosted accuracy further. Our methodological framework is transferable beyond Europe and adaptable to other classification systems. Future research should advance temporal modelling of dynamic habitats, extend to habitat segmentation and quality assessment, and exploit next-generation EO data paired with higher-quality in-situ observations.