SelvaMask：热带森林及其他地区树木分割
SelvaMask: Segmenting Trees in Tropical Forests and Beyond
AI总结：
SelvaMask通过引入新的热带数据集和模块化检测-分割流程，在密集热带森林中实现了最先进的树冠分割性能。
摘要：
热带森林孕育着地球大部分的树种多样性，并对全球生态平衡至关重要。特别是冠层树在碳储存和这些生态系统功能中扮演着不成比例的角色。大规模研究冠层树需要准确地界定单个树冠，通常使用高分辨率的航空影像进行。尽管在单个树冠分割方面取得了变压器模型的进步，但大多数森林，尤其是热带森林的性能仍然较低。为此，我们引入了SelvaMask，一个包含超过8,800个人工界定树冠的新热带数据集，涵盖巴拿马、巴西和厄瓜多尔三个新热带森林地点。SelvaMask具有全面的标注，包括标注者间一致性的评估，捕捉热带森林的密集结构，并强调了该任务的难度。利用这一基准，我们提出了一种模块化的检测-分割流程，利用视觉基础模型（VFMs），使用领域特定的检测-提示器。我们的方法达到了最先进的性能，优于零样本通用模型和完全监督的端到端方法在密集的热带森林中。我们在外部的热带和温带数据集上验证了这些收益，证明了SelvaMask既是具有挑战性的基准，也是通用森林监测的关键推动因素。我们的代码和数据集将公开发布。
英文摘要：
Tropical forests harbor most of the planet's tree biodiversity and are critical to global ecological balance. Canopy trees in particular play a disproportionate role in carbon storage and functioning of these ecosystems. Studying canopy trees at scale requires accurate delineation of individual tree crowns, typically performed using high-resolution aerial imagery. Despite advances in transformer-based models for individual tree crown segmentation, performance remains low in most forests, especially tropical ones. To this end, we introduce SelvaMask, a new tropical dataset containing over 8,800 manually delineated tree crowns across three Neotropical forest sites in Panama, Brazil, and Ecuador. SelvaMask features comprehensive annotations, including an inter-annotator agreement evaluation, capturing the dense structure of tropical forests and highlighting the difficulty of the task. Leveraging this benchmark, we propose a modular detection-segmentation pipeline that adapts vision foundation models (VFMs), using domain-specific detection-prompter. Our approach reaches state-of-the-art performance, outperforming both zero-shot generalist models and fully supervised end-to-end methods in dense tropical forests. We validate these gains on external tropical and temperate datasets, demonstrating that SelvaMask serves as both a challenging benchmark and a key enabler for generalized forest monitoring. Our code and dataset will be released publicly.
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。
