# Building Footprint Generation Using Improved Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1810.11224 · HTML: https://mlsanity.com/paper/1810.11224/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2102.11262/) (2021)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [Building Footprint Extraction with Graph Convolutional Network](https://mlsanity.com/paper/2305.04499/) (2023)
- [TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction](https://mlsanity.com/paper/2311.02617/) (2023)
- [CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets](https://mlsanity.com/paper/2401.04139/) (2024)

## Abstract (arXiv)

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameters tuning.