挪威高精度地理空间AI基准数据集，支持精细语义分割与检测
NordFKB: a fine-grained benchmark dataset for geospatial AI in Norway
- 基于全国权威地理数据库构建，含36类细粒度标注
- 覆盖7个地貌气候区，包含正射影像与框/掩码双标注
- 提供标准化评估工具，适合地图制图与空间规划研究
我们提出NordFKB，一个基于挪威国家级权威地理数据库Felles KartdataBase（FKB）的细粒度地理空间AI基准数据集。该数据集包含高分辨率正射影像，以及针对36个语义类别的详细标注，涵盖GeoTIFF格式的逐类二值分割掩码和COCO风格的边界框标注。数据采集自七个地理特征各异的区域，确保气候、地形与城市化程度的多样性。仅包含至少一个标注对象的图像块，训练与验证集通过跨区域随机采样生成，保障类别与场景分布的代表性。经人工专家审核与质量控制，确保标注准确性。同时发布包含标准化评估协议与工具的基准仓库，支持语义分割与目标检测任务的可复现比较研究。NordFKB为地图绘制、土地管理与空间规划中的AI方法发展提供坚实基础，并为未来扩展覆盖范围、时间维度及多模态数据铺路。
原文摘要 · Abstract (English)
We present NordFKB, a fine-grained benchmark dataset for geospatial AI in Norway, derived from the authoritative, highly accurate, national Felles KartdataBase (FKB). The dataset contains high-resolution orthophotos paired with detailed annotations for 36 semantic classes, including both per-class binary segmentation masks in GeoTIFF format and COCO-style bounding box annotations. Data is collected from seven geographically diverse areas, ensuring variation in climate, topography, and urbanization. Only tiles containing at least one annotated object are included, and training/validation splits are created through random sampling across areas to ensure representative class and context distributions. Human expert review and quality control ensures high annotation accuracy. Alongside the dataset, we release a benchmarking repository with standardized evaluation protocols and tools for semantic segmentation and object detection, enabling reproducible and comparable research. NordFKB provides a robust foundation for advancing AI methods in mapping, land administration, and spatial planning, and paves the way for future expansions in coverage, temporal scope, and data modalities.
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