利用图像生成器解决训练数据稀缺问题：Gen4Regen数据集用于森林再生制图
Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
AI总结：
本文通过Gen4Regen数据集和Nano Banana Pro模型生成高质量图像及语义掩码，解决森林再生物种分割中的数据稀缺和类别不平衡问题，提升F1分数15%以上。
摘要：
可持续森林管理依赖精确的物种组成制图，但传统地面调查劳动强度大且地理受限。尽管无人机提供可扩展的数据收集，但深度学习解释的过渡受专家标注图像严重稀缺的瓶颈限制，特别是在复杂且视觉异质的再生区。本文通过提供可扩展框架减少对人工照片解释的依赖，同时利用大规模视觉语言Nano Banana Pro模型生成高保真图像及其对应的像素对齐语义掩码。我们引入WilDReF-Q-V2数据集，包含13977张未标注和50张标注的现实图像，以及Gen4Regen数据集，包含2101对合成图像和语义掩码。我们的方法整合现实数据与AI生成图像，证明AI生成数据对现实数据的高互补性，统一训练使F1分数比纯监督基线提升超过15%。此外，我们展示即使少量提示生成数据也能显著提升低表示物种的性能，某些物种的F1分数提升高达30%。我们得出结论：视觉语言模型可作为敏捷数据生成器，有效启动感知任务，用于专家标签稀缺或不可用的 niche AI 领域。我们的数据集、源代码和模型将在https://norlab-ulaval.github.io/gen4regen提供。
英文摘要：
Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained. While Uncrewed Aerial Vehicles (UAVs) offer scalable data collection, the transition to deep learning-based interpretation is bottlenecked by the severe scarcity of expert-annotated imagery, particularly in complex, visually heterogeneous regeneration zones. This paper addresses the dual challenges of data scarcity and extreme class imbalance in the fine-grained semantic segmentation of plants by providing a scalable framework that reduces reliance on manual photo-interpretation for high-resolution, millimetre-level aerial imagery. Importantly, we leverage the large-scale Nano Banana Pro model to simultaneously generate high-fidelity images and their corresponding pixel-aligned semantic masks from prompts. We introduce WilDReF-Q-V2, an expansion of a natural forest dataset with 13 977 new unlabelled and 50 hand-labelled real images, as well as the Gen4Regen dataset, featuring 2101 pairs of synthetic images and semantic masks. Our methodology integrates real-world data with AI-generated images, highlighting that AI-generated data is highly complementary to real-world data, with unified training yielding an F1 score improvement of over 15 %pt compared to purely supervised baselines. Furthermore, we demonstrate that even small quantities of prompt-generated data significantly improve performance for underrepresented classes, some of which see per-class F1 score gains of over 30 %pt. We conclude that large-scale vision models can serve as agile data generators, effectively bootstrapping perception tasks for niche AI domains where expert labels are scarce or unavailable. Our datasets, source code, and models will be available at https://norlab-ulaval.github.io/gen4regen.
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。
