NordFKB: a fine-grained benchmark dataset for geospatial AI in Norway

TL;DR

NordFKB is a comprehensive, high-resolution geospatial dataset from Norway with detailed annotations, designed to facilitate research in semantic segmentation and object detection for geospatial AI applications.

Contribution

The paper introduces NordFKB, a new fine-grained benchmark dataset with diverse, high-quality annotations and a standardized evaluation framework for geospatial AI in Norway.

Findings

High annotation accuracy verified by experts

Diverse geographic coverage ensures broad applicability

Benchmarking tools enable reproducible research

Abstract

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.