# Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images

arXiv: https://arxiv.org/abs/1902.04604 · HTML: https://mlsanity.com/paper/1902.04604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Exploration of Convolutional Neural Network Architectures for Large Region Map Automation](https://mlsanity.com/paper/2211.03854/) (2022)
- [Beyond Static Costs: Learning-Dynamics Aware Loss Functions for Long-Tailed Classification](https://mlsanity.com/paper/2607.25830/) (2026)

## Abstract (arXiv)

Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training.