Biomazon：亚马逊盆地三维森林结构与生物量建模的多模态数据集
Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin
AI总结：
针对现有方法未将森林垂直结构作为有序轮廓学习的问题，提出Biomazon多模态基准数据集，结合GEDI RH和AGBD目标与多传感器预测因子，通过共享编码器-解码器框架进行消融研究，为热带森林结构一致RH轮廓预测和结构-生物量建模建立参考基准。
摘要：
准确、空间明确的描述热带森林结构对于碳核算和生态系统监测至关重要，然而大多数机器学习流程预测冠层顶部高度代理（例如RH95/RH98）或AGBD作为单独的标量目标，而不是将森林垂直结构作为有序轮廓学习。社区缺乏一个ML就绪的多模态基准，用于联合预测整个GEDI RH轮廓与AGBD，或评估强制RH百分位数之间物理一致排序的方法。我们通过Biomazon解决了这一问题，这是一个覆盖亚马逊盆地的20米多模态基准数据集，在标准化的空间划分和评估协议下，将GEDI RH和AGBD目标与多传感器预测因子（Sentinel-1/2、ALOS-2 PALSAR-2、Copernicus DEM、Dynamic World LULC和AlphaEarth嵌入）配对。使用共享编码器-解码器与任务特定头作为基线框架，我们对（i）骨干/模型规模、（ii）模态贡献以及（iii）在独立和融合设置下使用辅助嵌入进行了全面的消融研究，并报告了单目标和联合目标结果，以量化统一训练协议下的权衡。最后，我们通过与现有网格化产品（包括GEDI L4D RH10-RH98和AGBD）在匹配时间尺度上的区域对齐比较，将基线性能置于背景中。Biomazon连同随附的协议和基线结果，为未来热带森林中结构一致的RH轮廓预测和结构-生物量建模工作建立了参考基准。
英文摘要：
Accurate, spatially explicit characterization of tropical forest structure is essential for carbon accounting and ecosystem monitoring, yet most ML pipelines predict canopy-top height proxies (e.g., RH95/RH98) or AGBD as separate scalar targets, rather than learning the forest vertical structure as an ordered profile. The community lacks a ML-ready multimodal benchmark for predicting the entire GEDI RH profile jointly with AGBD, or for evaluating methods that enforce physically consistent ordering across RH percentiles. We address this with Biomazon, a 20 m multimodal benchmark dataset over the Amazon Basin that pairs GEDI RH and AGBD targets with multi-sensor predictors (Sentinel-1/2, ALOS-2 PALSAR-2, Copernicus DEM, Dynamic World LULC, and AlphaEarth embeddings) under standardized spatial splits and evaluation protocols. Using a shared encoder-decoder with task-specific heads as a baseline framework, we conduct a comprehensive ablation study of (i) backbone/model scale, (ii) modality contributions, and (iii) the use of auxiliary embeddings under standalone and fusion settings, and we report both single-target and joint-target results to quantify tradeoffs under a unified training protocol. Finally, we contextualize baseline performance through regionally aligned comparisons against existing gridded products, including GEDI L4D RH10-RH98 and AGBD, at matching temporal scale. Biomazon, together with the accompanying protocols and baseline results, establishes a reference benchmark for future work on structurally consistent RH-profile prediction and structure-biomass modeling in tropical forests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
