# What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1806.05129 · HTML: https://mlsanity.com/paper/1806.05129/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Skyeyes: Ground Roaming using Aerial View Images](https://mlsanity.com/paper/2409.16685/) (2024)
- [Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models](https://mlsanity.com/paper/2208.02089/) (2022)
- [Semantic-aware Network for Aerial-to-Ground Image Synthesis](https://mlsanity.com/paper/2308.06945/) (2023)
- [Bird Eye-View to Street-View: A Survey](https://mlsanity.com/paper/2405.08961/) (2024)
- [From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis](https://mlsanity.com/paper/2509.24369/) (2025)

## Abstract (arXiv)

This paper investigates conditional generative adversarial networks (cGANs) to overcome a fundamental limitation of using geotagged media for geographic discovery, namely its sparse and uneven spatial distribution. We train a cGAN to generate ground-level views of a location given overhead imagery. We show the "fake" ground-level images are natural looking and are structurally similar to the real images. More significantly, we show the generated images are representative of the locations and that the representations learned by the cGANs are informative. In particular, we show that dense feature maps generated using our framework are more effective for land-cover classification than approaches which spatially interpolate features extracted from sparse ground-level images. To our knowledge, ours is the first work to use cGANs to generate ground-level views given overhead imagery and to explore the benefits of the learned representations.