---
title: Semantic Inpainting for Satellite Images
emoji: 🎨
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 5.27.0
app_file: app.py
pinned: false
license: unknown
---
# Semantic Inpainting
**Semantic-Aware Image-to-Image Generation Using Diffusion Models for Urban Satellite Imagery**
A Gradio-based web app that lets you interactively mask and reconstruct regions of high-resolution urban satellite images. Under the hood, a latent diffusion model conditioned on semantic segmentation labels ensures that buildings, roads, forests, and other classes are inpainted in a contextually coherent and visually realistic way.
---
Link to the GitHub repository - [GitHub](https://github.com/kbuyukburc/SemanticInpaint)
Link to the Hugging Face (HF) demo - [Hugging Face](https://huggingface.co/spaces/Kutluhan/SemanticInpaint)
## Features
- **Interactive Gradio GUI**
- Upload or select example satellite images
- Free-hand drawing mask regions
- Semantic label selector with color preview
- Adjustable guidance scale for faithfulness to labels
- Choose number of outputs to generate
- **High-quality Inpainting**
- Latent diffusion conditioned on segmentation maps
- Supports common urban classes: buildings, roads, vegetation, water, etc.
- **Flexible Workflow**
- View original and inpainted images
- Download and save outputs
- “Use Output as New Input” button for iterative edits
---
## Getting Started
### Prerequisites
- Python >= 3.10
- git
- A CUDA-capable GPU (recommended for performance)
### Installation on local computer or using Google Colab or Kaggle with GPU (Preferred)
1. **Clone the HF repository**
```bash
sudo apt install git-lfs # install it to pull example images
git clone https://huggingface.co/spaces/Kutluhan/SemanticInpaint
# or
git clone https://github.com/kbuyukburc/SemanticInpaint
cd SemanticInpaint
```
2. **Install Python dependencies**
```bash
pip install -r requirements.txt
pip install gradio
```
### Run it directly on Hugging Face Spaces
1. **Visit to HF Spaces page using the [Link](https://huggingface.co/spaces/Kutluhan/SemanticInpaint)**
```bash
https://huggingface.co/spaces/Kutluhan/SemanticInpaint
```
2. **Create an account (requires PRO account for extra Zero-gpu credits) and run it directly (Skip Usage part 1)**
---
## Usage
1. **Launch the app**
```bash
python app.py
```
By default, Gradio will run on the public URL.
You can also select which pre trained model you want to use (default model008000.pt, finetuned on 30 epocs), available [here](https://huggingface.co/Kutluhan/SemanticInpaint/tree/main).
The pre-trained model can be changed in the app.py file.
2. **In your browser**
- **Input Panel**: Upload or pick an urban satellite image from examples.
- **Masking & Control Panel**:
- Draw to mask regions.
- Select semantic class (e.g., Building, Road, Vegetation).
- Adjust **Thickness Scale** for how strictly to follow labels.
- Choose **Number of Images** to generate.
- **Output Panel**:
- Compare original v/s. inpainted images.
- Download and save the output images or re-feed it for another pass.
---
## Sample Outputs
Here are some sample outputs based on our testing --
- Imagine you want to create new town with wider roads going through a forest
- Imagine you want to create land for permanent crops in a small town
- Imagine you want to create a new city in middle of nowhere that doesn't have any buildings, so you start with building roads
- Imagine you want create land for herbaceous vegetation in a crowded city
- Imagine there was a lot of rain in a particular area leading to water deposits and wetland formation, so you want to create a bridge connecting the two seperated villages
## Recommendations and Insights
### General Recommendations
- **Generate Multiple Samples**: When using the tool, generate 2 to 4 samples to explore different variations. Not all outputs are visually satisfying, so generating multiple samples increases the chance of obtaining a high-quality, contextually appropriate result.
### Label Behavior Insights
- **Multi-Label Combinations That Work Well**
- **Urban Fabric**: Tends to generate city-like structures and dense urban layouts.
- **Industrial/Commercial**: Often produces road networks and industrial complexes. Users should preferably draw thin lines to simulate roads when using this label.
- **Permanent Crops**: Generates brown or green agricultural fields.
- **Forests**: Creates dense forested regions.
- **Water**: Primarily generates lakes (greenish color) and occasionally seas (bluish color).
- **Artificial Non-Agricultural Vegetated Areas**: Complements urban layouts.
- **Labels That Work Well Alone**
- **Pastures**: Best used independently.
- **Arable Land**: Works well individually.
- **Herbaceous Vegetation**: Generates vegetation but sometimes generates white snowy textures.
- **Labels That Do Not Perform Well**
- **Mine/Dump/Construction**: Limited generation quality due to insufficient training data.
- **Complex & Mixed Cultivation Patterns**: Inconsistent variation causes inconsistent results.
- **Orchards**: insufficent training data.
- **Open Spaces with Little or No Vegetation**: Sparse or incoherent generation.
- **Recommended Combinations**
- **Wetlands + Water**: Generates more realistic transitions.
- **Urban Fabric + Industrial/Commercial**: This combination generates city layouts with roads, creating realistic urban environments.
## Best Practices for Using the Tool
- **Use Few Classes at a Time**: Pick 1–2 semantic classes initially, draw them on the mask, and generate images.
- **Iterative Editing**: Select the best image from the generated samples as the new base, then add 1–2 more semantic classes and repeat the generation process.
- **Preserve Context**: Using only 1–2 new mask types at a time helps maintain better visual and semantic context in the generated images.
- **Avoid Large Masks**: Drawing very large masks disrupts spatial coherence. Prefer smaller, targeted edits to preserve surrounding context and improve the realism of generated outputs.
---