license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- image-inpainting
- remote-sensing
- satellite
- vhr
- diffusion
- geospatial
library_name: diffusers
pipeline_tag: image-to-image
🛰️ KAO-DIFFSAT-VHR-v1
Diffusion-based Satellite Image Inpainting (VHR) — Baseline Release
🚀 Project website: https://kaopanboonyuen.github.io/KAO/
🛰️ Visual Overview
High-fidelity satellite image reconstruction across diverse scenes (urban, agricultural, coastal).
🌍 Overview
KAO-DIFFSAT-VHR-v1 is a satellite-domain adaptation of diffusion-based inpainting, designed for Very High Resolution (VHR) remote sensing imagery.
This release serves as a baseline model in our IEEE TGRS work:
KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image
🎉 Accepted to IEEE Transactions on Geoscience and Remote Sensing (TGRS)
🌏 Presentation at AOGS 2026, Japan
⚠️ Important:
This model is NOT the KAO method itself.
It is a Stable Diffusion–based inpainting backbone adapted for satellite imagery, used as one of the reference baselines in our study.
👉 The full KAO framework (training-free / prompt-free optimization) will be released separately.
🧠 Positioning
| Component | Description |
|---|---|
| This Repo | Diffusion baseline for satellite inpainting |
| KAO (Paper) | Optimization-based enhancement (no retraining required) |
| Future Release | KAO-integrated inference (no prompt dependency) |
🏗️ Base Model
This model is built upon:
- Stable Diffusion v2 Inpainting
- Provided via the Hugging Face ecosystem
We adapt the pipeline for remote sensing characteristics, including:
- Large spatial structures (roads, rivers, urban layouts)
- Texture consistency across wide regions
- Geometric coherence in aerial perspective
🚀 Key Features
- 🛰️ Satellite-aware inpainting behavior
- 🌆 Improved structural consistency for urban layouts
- 🌿 Better handling of vegetation and natural textures
- 🧩 Compatible with standard diffusion pipelines (Diffusers)
🔬 Results Overview
Qualitative comparison across multiple models. KAO demonstrates superior structural reconstruction and texture consistency.
Detailed comparison highlighting fine-grained structures and realistic spatial coherence.
🛰️ Full Benchmark Results
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Comparison across multiple scenes including urban, agricultural, and occluded environments.
KAO consistently preserves spatial structure, enhances texture fidelity, and improves reconstruction realism.
⚠️ What This Model Is (and Is Not)
✅ This model:
- A baseline diffusion model for satellite inpainting
- Used for comparative evaluation in our research
- Suitable for prompt-based reconstruction tasks
❌ This model is NOT:
- The full KAO algorithm
- A training-free optimization method
- A prompt-free system
👉 Those capabilities are introduced in our paper and will be released in future versions.
🚀 Usage (Diffusers)
from diffusers import StableDiffusionInpaintPipeline
import torch
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"kaopanboonyuen/KAO-DIFFSAT-VHR-v1",
torch_dtype=torch.float16
).to("cuda")
prompt = "High-resolution satellite image of urban area with missing regions reconstructed"
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask
).images[0]
🛰️ Prompt Guide (Satellite Domain)
Use prompts that enforce:
- spatial coherence
- structural realism
- geospatial consistency
🔹 Template
High-resolution satellite image of [SCENE],
reconstruct missing regions with realistic [STRUCTURE],
maintain spatial consistency, natural textures, and accurate geometry
🧪 Use Cases
- Cloud removal in satellite imagery
- Infrastructure reconstruction
- Map completion
- Environmental monitoring
- Disaster recovery (flood, wildfire)
⚠️ Limitations
- Prompt-sensitive behavior
- Not optimized for hyperspectral imagery
- High memory footprint (~25GB)
- No explicit geospatial constraints (baseline only)
🔮 Roadmap
- 🔜 KAO-DIFFSAT-v2 (KAO-integrated)
- 🔜 Prompt-free inference (as proposed in paper)
- 🔜 Lightweight and scalable variants
📚 Citation
🔹 Base Model
@misc{stabilityai2022sd2,
title = {Stable Diffusion v2},
author = {{Stability AI}},
year = {2022},
howpublished = {\url{https://github.com/Stability-AI/stablediffusion}},
note = {Including inpainting variant}
}
🔹 Our Work (KAO)
@article{panboonyuen2025kao,
title={KAO: Kernel-Adaptive Optimization in Diffusion for Satellite Image},
author={Panboonyuen, Teerapong},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2025},
publisher={IEEE}
}
🔬 Project Overview
This repository presents KAO-DIFFSAT-VHR-v1, a diffusion-based framework for very high-resolution (VHR) satellite image inpainting, designed for geospatial restoration and reconstruction tasks.
The model builds upon Stable Diffusion-based inpainting and adapts it for remote sensing imagery.
📄 Paper
📘 Full paper available on arXiv:
https://arxiv.org/abs/2511.02462
🌐 Project Page
🚀 Project website:
https://kaopanboonyuen.github.io/KAO/
🤝 Acknowledgements
- Stability AI for foundational diffusion models
- Hugging Face for open model hosting
- Remote sensing community for datasets and benchmarks
🌟 Vision
This project aims to advance:
Generative AI × Earth Observation
toward scalable, reliable, and interpretable satellite intelligence systems.