# GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis

arXiv: https://arxiv.org/abs/2404.06637 · HTML: https://mlsanity.com/paper/2404.06637/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 59 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 30 of 49)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16322/) (2023)
- [Training-Free Layout Control with Cross-Attention Guidance](https://mlsanity.com/paper/2304.03373/) (2023)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [Photorealistic Video Generation with Diffusion Models](https://mlsanity.com/paper/2312.06662/) (2023)
- [Diffusion Self-Guidance for Controllable Image Generation](https://mlsanity.com/paper/2306.00986/) (2023)
- [BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion](https://mlsanity.com/paper/2307.10816/) (2023)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)
- [Subject-driven Text-to-Image Generation via Apprenticeship Learning](https://mlsanity.com/paper/2304.00186/) (2023)

## Built on by (most cited first, 30 of 32)

- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [EarthSynth: Generating Informative Earth Observation with Diffusion Models](https://mlsanity.com/paper/2505.12108/) (2025)
- [Measuring the Intrinsic Dimension of Earth Representations](https://mlsanity.com/paper/2511.02101/) (2025)
- [Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance](https://mlsanity.com/paper/2408.04224/) (2024)
- [CC-Diff: Enhancing Contextual Coherence in Remote Sensing Image Synthesis](https://mlsanity.com/paper/2412.08464/) (2024)
- [MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data](https://mlsanity.com/paper/2504.07210/) (2025)
- [A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction](https://mlsanity.com/paper/2504.12112/) (2025)
- [VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics](https://mlsanity.com/paper/2511.07744/) (2025)
- [MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs](https://mlsanity.com/paper/2509.11662/) (2025)
- [Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?](https://mlsanity.com/paper/2501.15847/) (2025)
- [RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries](https://mlsanity.com/paper/2505.23283/) (2025)
- [TAMMs: Change Understanding and Forecasting in Satellite Image Time Series with Temporal-Aware Multimodal Models](https://mlsanity.com/paper/2506.18862/) (2025)
- [RSEdit: Text-Guided Image Editing for Remote Sensing](https://mlsanity.com/paper/2603.13708/) (2026)
- [Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data](https://mlsanity.com/paper/2604.07092/) (2026)
- [HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation](https://mlsanity.com/paper/2604.11444/) (2026)
- [Tessellating The Earth](https://mlsanity.com/paper/2606.27514/) (2026)
- [TerraDiT-Omega: Unified Spatial Control for Satellite Image Synthesis with Any Geospatial Primitive](https://mlsanity.com/paper/2606.31029/) (2026)
- [SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images](https://mlsanity.com/paper/2602.20412/) (2026)
- [A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders](https://mlsanity.com/paper/2604.18881/) (2026)
- [AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors](https://mlsanity.com/paper/2605.14341/) (2026)
- [MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling](https://mlsanity.com/paper/2605.20090/) (2026)
- [TerraDiT: Point-Conditioned Diffusion Transformer for Satellite Image Synthesis](https://mlsanity.com/paper/2603.02172/) (2026)
- [Generating Satellite Imagery Data for Wildfire Detection through Mask-Conditioned Generative AI](https://mlsanity.com/paper/2604.02479/) (2026)
- [HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition](https://mlsanity.com/paper/2604.19392/) (2026)
- [Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis](https://mlsanity.com/paper/2605.04557/) (2026)
- [Earth Science Foundation Models: From Perception to Reasoning and Discovery](https://mlsanity.com/paper/2605.12542/) (2026)
- [D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog](https://mlsanity.com/paper/2605.14326/) (2026)
- [CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery](https://mlsanity.com/paper/2606.00548/) (2026)
- [Beyond Edge Maps: Wavelet-Domain Conditioning for Multi-Adapter Map-to-Satellite Diffusion](https://mlsanity.com/paper/2608.00083/) (2026)
- [Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis](https://mlsanity.com/paper/2609.02683/) (2026)

## Abstract (arXiv)

We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control. The global style control is via textual prompts or geographic location. These enable the specification of scene semantics or regional appearance respectively, and can be used together. We train our model on a large dataset of paired satellite imagery, with automatically generated captions, and OpenStreetMap data. We evaluate various combinations of control inputs, including different types of layout controls. Results demonstrate that our model can generate diverse, high-quality images and exhibits excellent zero-shot generalization. The code and model checkpoints are available at https://github.com/mvrl/GeoSynth.