构建大规模遥感交通物体分割数据集，提升复杂场景下分割精度。
A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
- 构建涵盖4类交通物体的跨城市跨国家遥感数据集
- 新方法在多尺度和复杂环境下实现更精准分割
- 适合遥感图像分析与智能交通研究者使用
遥感影像在评估区域交通能力方面至关重要。然而，现有分割数据集在物体类别和场景多样性上不足，限制了模型对真实场景中交通能力的全面评估。为弥补这一缺陷，我们构建了一个大规模、多样化的交通物体分割数据集，命名为NWPU-Traffic。该数据集包含汽车、飞机、船舶和火车四类交通物体，覆盖7个国家49个城市的广泛场景，并提供实例级标注，确保个体物体的精确分割，有效解决了现有数据集在分辨率和场景多样性上的关键短板。基于该数据集，我们建立了多个主流分割网络的基准测试。此外，我们提出一种新型分割方法，融合空间-通道保持的特征交互机制与自适应特征解码器，显著提升了在不同尺度和复杂环境下的分割鲁棒性。大量实验与消融研究验证了方法的有效性。数据集与代码已公开于https://github.com/CVer-Yang/NWPU-Traffic。
原文摘要 · Abstract (English)
Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic. This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing datasets. Leveraging this dataset, we establish a benchmark with several popular segmentation networks. Furthermore, we propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Traffic.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。