用AI对比检测高山保护区生态变化，提升复杂地形监测效率。
Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures
- 用深度学习直接或分步检测地表变化，比较不同模型表现。
- 融合激光雷达数据使语义分割准确率从30%提升至50%。
- 适合需要长期生态监测的科研人员与自然保护机构使用。
高山生态系统受快速气候变化等扰动影响，亟需高频次栖息地监测，但人工制图成本过高难以满足时间分辨率需求。本文利用奥地利盖萨塞国家公园的长期高山栖息地数据，采用深度学习方法进行变化检测，填补了地理空间基础模型（GFMs）在边界模糊、类别极度不平衡的复杂自然环境中的应用空白。比较了后分类变化检测（CD）与直接变化检测两种范式：后分类中评估了普里蒂维-EO-2.0和Clay v1.0 GFMs与U-Net CNN的性能；直接检测中测试了Transformer结构的ChangeViT与U-Net基线。基于覆盖15.3 km²、包含4,480处已记录变化的高分辨率多模态数据（RGB、NIR、LiDAR、地形属性），结果表明，Clay v1.0在多类栖息地变化检测中达到51%总体准确率，优于U-Net的41%；二值变化检测准确率均为67%。直接检测在二值场景下交并比（IoU）达0.53，远超U-Net的0.35，但多类检测准确率仅28%。跨时序评估显示，Clay模型在2020年数据上保持33%准确率，优于U-Net的23%。融合LiDAR数据使语义分割准确率从30%提升至50%。尽管整体准确率低于均质区域，但仍反映了复杂高山环境的真实性能。未来工作将结合基于对象的后处理与物理约束以提升实用性。
原文摘要 · Abstract (English)
Rapid climate change and other disturbances in alpine ecosystems demand frequent habitat monitoring, yet manual mapping remains prohibitively expensive for the required temporal resolution. We employ deep learning for change detection using long-term alpine habitat data from Gesaeuse National Park, Austria, addressing a major gap in applying geospatial foundation models (GFMs) to complex natural environments with fuzzy class boundaries and highly imbalanced classes. We compare two paradigms: post-classification change detection (CD) versus direct CD. For post-classification CD, we evaluate GFMs Prithvi-EO-2.0 and Clay v1.0 against U-Net CNNs; for direct CD, we test the transformer ChangeViT against U-Net baselines. Using high-resolution multimodal data (RGB, NIR, LiDAR, terrain attributes) covering 4,480 documented changes over 15.3 km2, results show Clay v1.0 achieves 51% overall accuracy versus U-Net's 41% for multi-class habitat change, while both reach 67% for binary change detection. Direct CD yields superior IoU (0.53 vs 0.35) for binary but only 28% accuracy for multi-class detection. Cross-temporal evaluation reveals GFM robustness, with Clay maintaining 33% accuracy on 2020 data versus U-Net's 23%. Integrating LiDAR improves semantic segmentation from 30% to 50% accuracy. Although overall accuracies are lower than in more homogeneous landscapes, they reflect realistic performance for complex alpine habitats. Future work will integrate object-based post-processing and physical constraints to enhance applicability.
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