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

Published: 3 July 2025| Version 1 | DOI: 10.17632/9kbc6zdn7b.1
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Description

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.

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Institutions

University of Milano-Bicocca

Departments

Department of Informatics, Systems and Communication

Categories

Computer Vision, Use of Computers in Earth Sciences, Remote Sensing, Image Segmentation, Machine Learning, Pattern Recognition, Deep Learning, Image Analysis

Funding

European Union- Next Generation EU, Mission 4 Component 1

F53D23010780001

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