# Segmentation of Roads in Satellite Images using specially modified U-Net CNNs

Source: [Segmentation of Roads in Satellite Images using specially modified U-Net CNNs](<https://www.sota2.com/research/paper/68fa6af11e209131cea2b3a6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:10:35.496Z

Authors: Jonas Bokstaller, Yihang She, Zhehan Fu, Tommaso Macr\\\`i

Published: 2021-09-29T19:08:32.000Z

## Abstract

The image classification problem has been deeply investigated by the research community, with computer vision algorithms and with the help of Neural Networks. The aim of this paper is to build an image classifier for satellite images of urban scenes that identifies the portions of the images in which a road is located, separating these portions from the rest. Unlike conventional computer vision algorithms, convolutional neural networks (CNNs) provide accurate and reliable results on this task. Our novel approach uses a sliding window to extract patches out of the whole image, data augmentation for generating more training/testing data and lastly a series of specially modified U-Net CNNs. This proposed technique outperforms all other baselines tested in terms of mean F-score metric.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.14671>)

[PDF](<https://arxiv.org/pdf/2109.14671.pdf>)