Abstract
Referring expression segmentation is a fundamental task in computer vision that integrates natural language understanding with precise visual localization of target regions. Considering aerial imagery (e.g., modern aerial photos collected through drones, historical photos from aerial archives, high-resolution satellite imagery, etc.) presents unique challenges because spatial resolution varies widely across datasets, the use of color is not consistent, targets often shrink to only a few pixels, and scenes contain very high object densities and objects with partial occlusions. This work presents Aerial-D, a new large-scale referring expression segmentation dataset for aerial imagery, comprising 37,288 images with 1,522,523 referring expressions that cover 259,709 annotated targets, spanning across individual object instances, groups of instances, and semantic regions covering 21 distinct classes that range from vehicles and infrastructure to land coverage types. The dataset was constructed through a fully automatic pipeline that combines systematic rule-based expression generation with a Large Language Model (LLM) enhancement procedure that enriched both the linguistic variety and the focus on visual details within the referring expressions. Filters were additionally used to simulate historic imaging conditions for each scene. We adopted the RSRefSeg architecture, and trained models on Aerial-D together with prior aerial datasets, yielding unified instance and semantic segmentation from text for both modern and historical images. Results show that the combined training achieves competitive performance on contemporary benchmarks, while maintaining strong accuracy under monochrome, sepia, and grainy degradations that appear in archival aerial photography. The dataset, trained models, and complete software pipeline are publicly available at this https URL .
Abstract (translated)
参考表达分割是计算机视觉中的一个基础任务，它将自然语言理解与目标区域的精确视觉定位相结合。考虑到空中图像（例如通过无人机收集的现代航拍照片、历史档案中的照片、高分辨率卫星图像等）带来了独特的挑战，因为这些数据集的空间分辨率变化很大，颜色使用不一致，目标通常缩小到只有几个像素，并且场景包含非常高的物体密度和部分遮挡的物体。这项工作介绍了一个新的大规模参考表达分割数据集Aerial-D，该数据集专门针对空中图像设计，包括37,288张图片、1,522,523个参考表达式，这些表达覆盖了259,709个标注的目标，涵盖单个对象实例、一组实例以及21种不同的语义区域类别，从车辆和基础设施到土地覆盖类型。该数据集是通过一个全自动流程构建的，结合系统规则生成参考表达，并使用大型语言模型（LLM）增强程序来丰富参考表达的语言多样性并强调视觉细节中的关注点。此外还采用了过滤器模拟每个场景的历史成像条件。 我们采用RSRefSeg架构，在Aerial-D和先前的空中图像数据集上联合训练了模型，从而为现代和历史图像从文本中实现了统一的对象实例和语义分割。结果表明，组合训练在当代基准测试中取得了具有竞争力的表现，并且在黑白、棕褐色和颗粒状退化等历史航空摄影中的图像条件下仍保持了强大的准确性。该数据集、经过训练的模型以及完整的软件流程均可通过此链接公开获取：[在此插入实际URL]。
URL
https://arxiv.org/abs/2512.07338
