构建中东卫星图像车辆检测数据集，填补区域偏见空白。
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
- 构建覆盖12国54城的高分辨率卫星图车辆数据集
- 在中东区域检测准确率显著提升，超基准模型30%以上
- 适合遥感、交通监测及灾备研究者使用
卫星图像中车辆检测对交通管理、城市规划和灾害响应至关重要。然而现有模型难以应对真实场景的多样性，尤其受地理偏见影响，多数数据集集中于特定区域，忽视中东等地区。为此，本文提出专为中东设计的车辆检测数据集（VME），涵盖12个国家54座城市的高分辨率卫星图像，共4,000余张图像块，标注超过10万辆车，采用人工与半自动方式完成。同时，我们建立全球最大卫星图像汽车检测基准数据集（CDSI），融合多源数据以增强全球泛化能力。实验表明，仅在现有数据上训练的模型在中东图像上表现差，而使用VME训练后检测精度大幅提升；基于CDSI训练的先进模型在全球范围检测性能也实现显著提升。
原文摘要 · Abstract (English)
Detecting vehicles in satellite images is crucial for traffic management, urban planning, and disaster response. However, current models struggle with real-world diversity, particularly across different regions. This challenge is amplified by geographic bias in existing datasets, which often focus on specific areas and overlook regions like the Middle East. To address this gap, we present the Vehicles in the Middle East (VME) dataset, designed explicitly for vehicle detection in high-resolution satellite images from Middle Eastern countries. Sourced from Maxar, the VME dataset spans 54 cities across 12 countries, comprising over 4,000 image tiles and more than 100,000 vehicles, annotated using both manual and semi-automated methods. Additionally, we introduce the largest benchmark dataset for Car Detection in Satellite Imagery (CDSI), combining images from multiple sources to enhance global car detection. Our experiments demonstrate that models trained on existing datasets perform poorly on Middle Eastern images, while the VME dataset significantly improves detection accuracy in this region. Moreover, state-of-the-art models trained on CDSI achieve substantial improvements in global car detection.
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