Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventions and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open-access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than 83,000 manually labeled crowns - an order of magnitude larger than all previous tropical forest datasets combined. Extensive benchmarks on SelvaBox reveal two key findings: (1) higher-resolution inputs consistently boost detection accuracy; and (2) models trained exclusively on SelvaBox achieve competitive zero-shot detection performance on unseen tropical tree crown datasets, matching or exceeding competing methods. Furthermore, jointly training on SelvaBox and three other datasets at resolutions from 3 to 10 cm per pixel within a unified multi-resolution pipeline yields a detector ranking first or second across all evaluated datasets. Our dataset, code, and pre-trained weights are made public.

翻译：在热带森林中检测单个树冠对于研究这些受人类干预和气候变化影响的复杂且至关重要的生态系统至关重要。然而，热带树冠在大小、结构和形态上差异巨大，且普遍存在重叠与交错，这需要将先进的遥感方法应用于高分辨率影像。尽管对热带树冠检测的兴趣日益增长，但带标注的数据集仍然稀缺，阻碍了稳健模型的开发。我们推出了SelvaBox，这是用于高分辨率无人机影像中热带树冠检测的最大开放获取数据集。它覆盖三个国家，包含超过83,000个手动标注的树冠——其规模比之前所有热带森林数据集的总和还要大一个数量级。在SelvaBox上进行的大量基准测试揭示了两个关键发现：（1）更高分辨率的输入能持续提升检测精度；（2）仅在SelvaBox上训练的模型，在未见过的热带树冠数据集上实现了具有竞争力的零样本检测性能，达到或超过了现有竞争方法。此外，在一个统一的多分辨率流程中，联合使用SelvaBox和另外三个分辨率在每像素3至10厘米范围内的数据集进行训练，所得检测器在所有评估的数据集上均排名第一或第二。我们的数据集、代码和预训练权重均已公开。