# Geospecific View Generation -- Geometry-Context Aware High-resolution Ground View Inference from Satellite Views

arXiv: https://arxiv.org/abs/2407.08061 · HTML: https://mlsanity.com/paper/2407.08061/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Pivotal Tuning for Latent-based Editing of Real Images](https://mlsanity.com/paper/2106.05744/) (2021)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [OneFormer: One Transformer to Rule Universal Image Segmentation](https://mlsanity.com/paper/2211.06220/) (2022)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Learning What and Where to Draw](https://mlsanity.com/paper/1610.02454/) (2016)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Cross-View Image Synthesis using Conditional GANs](https://mlsanity.com/paper/1803.03396/) (2018)
- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer](https://mlsanity.com/paper/2307.08015/) (2023)
- [Semantic Cross-View Matching](https://mlsanity.com/paper/1511.00098/) (2015)
- [Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs](https://mlsanity.com/paper/2303.14672/) (2023)
- [Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion](https://mlsanity.com/paper/2401.10786/) (2024)
- [Cross-View Panorama Image Synthesis](https://mlsanity.com/paper/2203.11832/) (2022)
- [Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image](https://mlsanity.com/paper/2012.06628/) (2020)
- [Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation](https://mlsanity.com/paper/2110.10183/) (2021)

## Built on by (most cited first, 13 of 13)

- [Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery](https://mlsanity.com/paper/2510.15869/) (2025)
- [Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion](https://mlsanity.com/paper/2507.04403/) (2025)
- [Leveraging BEV Paradigm for Ground-to-Aerial Image Synthesis](https://mlsanity.com/paper/2408.01812/) (2024)
- [Seeing through Satellite Images at Street Views](https://mlsanity.com/paper/2505.17001/) (2025)
- [Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagery](https://mlsanity.com/paper/2511.11470/) (2025)
- [Skyeyes: Ground Roaming using Aerial View Images](https://mlsanity.com/paper/2409.16685/) (2024)
- [Satellite to GroundScape -- Large-scale Consistent Ground View Generation from Satellite Views](https://mlsanity.com/paper/2504.15786/) (2025)
- [Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image](https://mlsanity.com/paper/2606.24138/) (2026)
- [AerialGo: Walking-through City View Generation from Aerial Perspectives](https://mlsanity.com/paper/2412.00157/) (2024)
- [SatDreamer360: Multiview-Consistent Generation of Ground-Level Scenes from Satellite Imagery](https://mlsanity.com/paper/2506.00600/) (2025)
- [Top2Pano: Learning to Generate Indoor Panoramas from Top-Down View](https://mlsanity.com/paper/2507.21371/) (2025)
- [Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images](https://mlsanity.com/paper/2605.19656/) (2026)
- [GeoFidelity-Bench: Evaluating Block-Conditioned Geographic Fidelity in Text-to-Image Street-View Generation](https://mlsanity.com/paper/2606.23669/) (2026)

## Abstract (arXiv)

Predicting realistic ground views from satellite imagery in urban scenes is a challenging task due to the significant view gaps between satellite and ground-view images. We propose a novel pipeline to tackle this challenge, by generating geospecifc views that maximally respect the weak geometry and texture from multi-view satellite images. Different from existing approaches that hallucinate images from cues such as partial semantics or geometry from overhead satellite images, our method directly predicts ground-view images at geolocation by using a comprehensive set of information from the satellite image, resulting in ground-level images with a resolution boost at a factor of ten or more. We leverage a novel building refinement method to reduce geometric distortions in satellite data at ground level, which ensures the creation of accurate conditions for view synthesis using diffusion networks. Moreover, we proposed a novel geospecific prior, which prompts distribution learning of diffusion models to respect image samples that are closer to the geolocation of the predicted images. We demonstrate our pipeline is the first to generate close-to-real and geospecific ground views merely based on satellite images.