SegFVG: A High-Resolution Large-Scale Dataset for Building Segmentation from Aerial Imagery in Northeastern Italy

Accurate building extraction from high-resolution aerial imagery is essential for numerous applications in remote sensing, urban planning, and disaster management. While AI-based methods enable fast, scalable, and cost-effective segmentation of building footprints, their development is often limited by the scarce availability of large-scale, geographically diverse datasets with reliable pixel-level annotations. In this work, we present SegFVG, a large-scale, high-resolution, and geographically diverse dataset for building segmentation, focused on the Friuli Venezia Giulia region in northeastern Italy. The dataset includes over 15,000 true orthophoto aerial image tiles, each of size 2000 × 2000 pixels with a ground sampling distance of 0.1 meters, paired with precise pixel-level building segmentation masks. Covering approximately 616 square kilometers, SegFVG captures a broad spectrum of urban, suburban, and rural settings across varied landscapes, including mountainous, flat, and coastal areas. Alongside the dataset, we provide benchmark results using several deep learning models. These support the usability of SegFVG for the development of accurate segmentation models and serve as a baseline to accelerate future research in building segmentation.从高分辨率航空影像中精准提取建筑物，对于遥感、城市规划及灾害管理等众多应用场景而言至关重要。尽管基于人工智能（AI）的方法可实现快速、可扩展且成本高效的建筑基底分割，但这类方法的发展往往因大规模、地理分布多样且带有可靠像素级标注的数据集可用量稀缺而受限。 在本研究中，我们提出了面向建筑分割任务的大规模、高分辨率且地理分布多样的数据集SegFVG，其聚焦于意大利东北部的弗留利-威尼斯朱利亚（Friuli Venezia Giulia）大区。该数据集包含超过15000张真实正射航空影像瓦片，单张影像尺寸为2000×2000像素，地面采样距离（ground sampling distance）为0.1米，同时配有精准的像素级建筑分割掩码（mask）。SegFVG覆盖面积约616平方公里，涵盖山地、平原、海岸等多样地貌下的城市、郊区与乡村等多种场景。除数据集外，我们还提供了基于多款深度学习模型的基准测试结果（benchmark results）。这些结果既验证了SegFVG在精准分割模型开发中的可用性，也可作为基线基准，以推动未来建筑分割领域的相关研究。