README.md

---
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

<p align="center">
  <img src="img/re_show_01.png" width="100%">
  <img src="img/re_show_02.png" width="100%">
</p>

<p align="center">
  <b>High-fidelity satellite image reconstruction across diverse scenes (urban, agricultural, coastal).</b>
</p>

---

## 🌍 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

<p align="center">
  <img src="img/re_show_01.png" width="90%">
</p>

**Qualitative comparison across multiple models. KAO demonstrates superior structural reconstruction and texture consistency.**

<p align="center">
  <img src="img/re_show_02.png" width="90%">
</p>

**Detailed comparison highlighting fine-grained structures and realistic spatial coherence.**

---

## 🛰️ Full Benchmark Results

<table>
<tr>
<td><img src="img/re_all_01.png"></td>
<td><img src="img/re_all_02.png"></td>
</tr>
<tr>
<td><img src="img/re_all_03.png"></td>
<td><img src="img/re_all_04.png"></td>
</tr>
<tr>
<td><img src="img/re_all_05.png"></td>
<td><img src="img/re_all_06.png"></td>
</tr>
</table>

<p align="center">
  <b>Comparison across multiple scenes including urban, agricultural, and occluded environments.</b><br>
  KAO consistently preserves spatial structure, enhances texture fidelity, and improves reconstruction realism.
</p>

---

## ⚠️ 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)

```python
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

```bibtex
@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)

```bibtex
@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.

---