AI 中文总结
研究在科特迪瓦评估可可映射，对比超高分辨率图像、十米级地球观测数据等在不同景观条件下的表现。通过开发模型并分层评估，发现VHR模型性能最佳，有针对性的VHR采集在复杂景观中有益，基础模型嵌入为大面积映射提供替代方案。
AI 中文摘要
准确的可可映射对于森林砍伐监测、供应链透明度和监管应用越来越重要。传统中分辨率地球观测（EO）图像中的空间聚合可能会限制在异质小农户景观中检测可可。因此，在科特迪瓦，我们评估了映射性能如何随景观条件变化，超高分辨率（VHR）图像是否提供了有意义的优势，以及基础模型嵌入是否能改善十米级可可映射。我们使用0.5米的昴宿星VHR图像、10米的哨兵2年度合成图像以及来自TESSERA和AlphaEarth Foundations（AEF）的嵌入开发了模型，并评估了四种公开可用的可可映射产品。通过使用分布在树木覆盖密度和景观破碎度梯度上的2821个独立解释的参考点进行景观分层精度评估来评估性能。VHR模型性能最高（F1 = 0.92），在所有层次上F1分数均保持在0.90以上。在十米级输入中，TESSERA表现最佳（F1 = 0.86），其次是AEF（F1 = 0.82）和哨兵2（F1 = 0.76）。在现有可可产品中，Kalischek产品表现最佳（F1 = 0.83），与内部训练的AEF模型相当。VHR和十米级方法之间的性能差异随着破碎度以及低和高树木覆盖密度条件而增加。因此，有针对性的VHR采集在复杂可可景观中可能特别有益，而基础模型嵌入为大面积映射提供了可扩展的替代方案。
英文摘要
Accurate cocoa mapping is increasingly important for deforestation monitoring, supply-chain transparency, and regulatory applications. Spatial aggregation in conventional medium-resolution Earth observation (EO) imagery may limit cocoa detection in heterogeneous smallholder landscapes. In Cote d'Ivoire, we therefore evaluated how mapping performance varies across landscape conditions, whether very high resolution (VHR) imagery provides a meaningful advantage, and whether foundation-model embeddings improve decametric cocoa mapping. We developed models using 0.5 m Pleiades VHR imagery, a 10 m Sentinel-2 annual composite, and embeddings from TESSERA and AlphaEarth Foundations (AEF), and additionally assessed four publicly available cocoa mapping products. Performance was evaluated through a landscape-stratified accuracy assessment using 2,821 independently interpreted reference points distributed across gradients of tree cover density and landscape fragmentation. The VHR model achieved the highest performance (F1 = 0.92) and maintained F1-scores above 0.90 across all strata. Among the decametric inputs, TESSERA performed best (F1 = 0.86), followed by AEF (F1 = 0.82) and Sentinel-2 (F1 = 0.76). Of the existing cocoa products, the Kalischek product performed best (F1 = 0.83), comparable to the internally trained AEF model. Performance differences between VHR and decametric approaches increased with fragmentation and under low and high tree cover density conditions. Targeted VHR acquisition may therefore be particularly beneficial in complex cocoa landscapes, while foundation-model embeddings offer a scalable alternative for large-area mapping.
