# A high-resolution large-scale dataset for building segmentation from aerial imagery in northeastern Italy

## TL;DR

This paper introduces SegFVG, a large-scale dataset for building segmentation from high-resolution aerial images in northeastern Italy, supporting AI-based urban and disaster management applications.

## Contribution

SegFVG is a new, large-scale, high-resolution dataset with precise pixel-level annotations for building segmentation in diverse geographical settings.

## Key findings

- SegFVG includes over 15,000 aerial image tiles with 0.1-meter resolution and pixel-level building masks.
- The dataset covers 616 km2 across urban, suburban, and rural areas in varied landscapes.
- Benchmark results with deep learning models demonstrate the dataset's utility for segmentation research.

## Abstract

Accurate building segmentation 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 km2, 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.