# Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery

arXiv: https://arxiv.org/abs/1902.06923 · HTML: https://mlsanity.com/paper/1902.06923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/2404.11302/) (2024)
- [Semantic-aware Network for Aerial-to-Ground Image Synthesis](https://mlsanity.com/paper/2308.06945/) (2023)
- [Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation](https://mlsanity.com/paper/2502.06288/) (2025)

## Abstract (arXiv)

This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image. We propose a novel conditional generative adversarial network (cGAN) in which the trained generator generates realistic looking and representative ground-level images using overhead imagery as auxiliary information. The generator is an encoder-decoder network which allows us to compare low- and high-level features as well as their concatenation for encoding the overhead imagery. We also demonstrate how our framework can be used to perform land cover classification by modifying the trained cGAN to extract features from overhead imagery. This is interesting because, although we are using this modified cGAN as a feature extractor for overhead imagery, it incorporates knowledge of how locations look from the ground.