Abstract
Semantic segmentation of high-resolution remote-sensing imagery is critical for urban mapping and land-cover monitoring, yet training data typically exhibits severe long-tailed pixel imbalance. In the dataset LoveDA, this challenge is compounded by an explicit Urban/Rural split with distinct appearance and inconsistent class-frequency statistics across domains. We present a prompt-controlled diffusion augmentation framework that synthesizes paired label--image samples with explicit control of both domain and semantic composition. Stage~A uses a domain-aware, masked ratio-conditioned discrete diffusion model to generate layouts that satisfy user-specified class-ratio targets while respecting learned co-occurrence structure. Stage~B translates layouts into photorealistic, domain-consistent images using Stable Diffusion with ControlNet guidance. Mixing the resulting ratio and domain-controlled synthetic pairs with real data yields consistent improvements across multiple segmentation backbones, with gains concentrated on minority classes and improved Urban and Rural generalization, demonstrating controllable augmentation as a practical mechanism to mitigate long-tail bias in remote-sensing segmentation. Source codes, pretrained models, and synthetic datasets are available at \href{this https URL}{Github}
Abstract (translated)
高分辨率遥感图像的语义分割对于城市制图和土地覆盖监测至关重要，然而训练数据通常表现出严重的长尾像素不平衡问题。在LoveDA数据集中，这一挑战进一步被明确的城市/农村划分所加剧，这些区域具有不同的外观特征以及跨领域的不一致类别频率统计。 我们提出了一种受提示控制的扩散增强框架，该框架能够生成带有明确领域和语义成分控制的配对标签-图像样本。第一阶段（Stage A）使用了具备领域感知能力的、条件于掩码比例的离散扩散模型来生成满足用户指定类别比例目标的布局，并同时保持学习到的共现结构。第二阶段（Stage B）则利用Stable Diffusion结合ControlNet指导，将这些布局转换为逼真的且符合相应领域的图像。 通过将这种比率和领域受控合成对与真实数据混合使用，我们观察到了多个分割骨干网络的一致性改进，并且这些改进主要集中在少数类上以及城市和农村区域的泛化能力得到提升。这表明可控增强作为一种实用机制能够有效缓解遥感分割中的长尾偏差问题。 源代码、预训练模型及合成数据集可在Github上的[此链接](https://github.com/yourusername/repo)获取。
URL
https://arxiv.org/abs/2602.04749
