satellite-building-segmentation

Satellite Building Segmentation

A high-performance satellite building segmentation model using enhanced U-Net architecture, achieving 65.62% Mean IoU on the ISPRS Potsdam dataset.

Model Performance

• Mean IoU: 65.62%
• Pixel Accuracy: 82.45%
• Training: 43 epochs with early stopping
• Architecture: Enhanced U-Net with multi-scale features
• Dataset: ISPRS Potsdam (6-class segmentation)

lass Performance

Model Details

Architecture

• Base: Enhanced U-Net
• Features: Multi-scale blocks, skip connections
• Input: RGB satellite images (512x512)
• Output: 6-class segmentation masks
• Parameters: ~31M parameters

Training Details

• Dataset: ISPRS Potsdam 2D Semantic Labeling
• Resolution: 5cm per pixel
• Epochs: 43 (early stopping)
• Batch Size: 4 (thermal optimized for RTX 3090)
• Loss: Combined Focal + Dice with class weights
• Optimizer: Adam with differential learning rates
• Hardware: NVIDIA RTX 3090

Usage

Quick Start

import torch
from PIL import Image
import numpy as np

# Load model
model = torch.load('pytorch_model.bin', map_location='cpu')
model.eval()

# Load and preprocess image
image = Image.open('satellite_image.tif').convert('RGB')
image = image.resize((512, 512))
image_tensor = torch.from_numpy(np.array(image)).float().permute(2, 0, 1) / 255.0

# Normalize
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
image_tensor = (image_tensor - mean) / std

# Predict
with torch.no_grad():
    outputs = model(image_tensor.unsqueeze(0))
    predictions = torch.argmax(torch.softmax(outputs, dim=1), dim=1)

# Convert to numpy
segmentation = predictions.cpu().numpy()[0]

Class Mapping

CLASS_COLORS = {
    0: [255, 255, 255],  # Impervious (white)
    1: [255, 0, 0],      # Buildings (red)
    2: [0, 255, 0],      # Low vegetation (green)
    3: [0, 255, 255],    # Trees (cyan)
    4: [255, 255, 0],    # Cars (yellow)
    5: [255, 0, 255],    # Clutter (magenta)
}

Technical Specifications

Input Requirements

• Format: RGB TIFF or PNG images
• Size: Any size (automatically resized to 512x512)
• Channels: 3 (RGB)
• Bit Depth: 8-bit recommended

Output Format

• Type: Integer class indices (0-5)
• Size: 512x512
• Classes: 6 semantic classes

Performance Characteristics

• Inference Speed: ~50ms per image (GPU)
• Memory Usage: ~2GB GPU memory
• Accuracy: Best on urban/suburban scenes

Citation

If you use this model in your research, please cite:

@misc{satellite-building-segmentation-2024,
  title={Satellite Building Segmentation using Enhanced U-Net},
  author={Your Name},
  year={2024},
  howpublished={Hugging Face Hub},
  url={https://huggingface.co/your-username/satellite-building-segmentation}
}

Contributing

Contributions welcome! Areas for improvement:

• Multi-scale inference
• Attention mechanism optimization
• Additional datasets
• Model compression
• Real-time inference

License

MIT License - See LICENSE file for details.

Acknowledgments

• ISPRS for the Potsdam dataset
• PyTorch community
• Satellite imagery research community
• Enhanced U-Net architecture research