DeepLabV3+ LULC Segmentation is a production-ready, state-of-the-art semantic segmentation framework for Land Use/Land Cover mapping from satellite imagery, offering end-to-end solutions from data preprocessing to intelligent land cover analysis

DeepLabV3+ LULC Segmentation

Tip

New in v2.0: Multi-architecture support with U-Net variants, advanced data augmentation pipeline, and production-ready web interface for real-time land cover analysis.

The DeepLabV3+ LULC Technical Report is now available. See details at: DeepLabV3+ for LULC Segmentation

DeepLabV3+ LULC Segmentation converts satellite imagery into structured land cover maps with industry-leading accuracy powering environmental monitoring applications for researchers, government agencies, and enterprises worldwide. Integration into leading geospatial projects, this framework has become the premier solution for developers building intelligent land cover analysis systems in the remote sensing era.

Core Features

• DeepLabV3+ with EfficientNet-B2 — State-of-the-Art LULC Segmentation
  Single model achieves 84.01% pixel accuracy across 8 land cover classes with 55.31% mIoU. Handles complex landscape patterns from urban areas to natural environments.

• Multi-Architecture Support — Flexible Model Selection
  Choose from DeepLabV3+, U-Net with ResNet34, and U-Net with SegFormer encoders. Each architecture optimized for different deployment scenarios and accuracy requirements.

• Production-Ready Pipeline — From Research to Deployment
  Complete framework with Flask web interface, batch processing, and comprehensive evaluation metrics. Seamlessly transition from model training to production deployment.

📣 Recent Updates

2025.01.15: Release of LULC Segmentation v2.0, includes:

• Multi-Architecture Framework:

  • Enhanced DeepLabV3+ with EfficientNet-B2, achieving 55.31% mIoU with custom SE-attention mechanism
  • U-Net with ResNet34, optimized for balanced performance with comprehensive evaluation metrics
  • Advanced training pipeline with PyTorch Lightning integration and mixed precision training

• Advanced Training Pipeline:

  • Integrated sophisticated data preprocessing with satellite-specific normalization
  • Smart augmentation strategies including geometric and photometric transformations
  • Comprehensive evaluation framework with detailed per-class analysis and confusion matrices

• Production Features:

  • Interactive Flask Web Application with drag-and-drop inference and real-time visualization
  • Batch processing capabilities for large-scale satellite image analysis
  • Model comparison tools and comprehensive performance benchmarking

Full History Log

⚡ Quick Start

1. Try Online Demo

2. Installation

Important

Pre-trained models are stored using Git LFS. Make sure you have Git LFS installed before cloning the repository to download the actual model files.

Install Git LFS and PyTorch following the official guide, then clone and set up the repository:

# Install Git LFS (if not already installed)
git lfs install

# Clone the repository (Git LFS will automatically download model files)
git clone https://github.com/VishalPainjane/deeplabv3-lulc-segmentation.git
cd deeplabv3-lulc-segmentation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

🔁 For Future Usage

Whenever you clone this repo on a different machine or re-clone it:

git lfs install
git clone https://github.com/VishalPainjane/deeplabv3-lulc-segmentation.git

Git will automatically pull the actual large files tracked by LFS.

3. Web Application

Launch the interactive Flask web interface:

# Start the web application
python app.py

# Application will be available at http://localhost:5000

Access the web interface for:

• Drag & drop image upload
• Real-time LULC segmentation
• Interactive result visualization
• Model performance metrics
• Download prediction results

📊 Performance Benchmark

All models trained on SEN-2 LULC preprocessed dataset for 50 epochs with advanced augmentation pipeline.

Model Comparison

Model	Encoder	mIoU	Pixel Acc	Params	GPU Memory	Inference (ms)	Model File
DeepLabV3+	EfficientNet-B2	55.31	84.01%	8.1M	3.2GB	45	deeplabv3_effecientnet_b2.pth
U-Net	ResNet34	46.12	81.24%	24.4M	5.1GB	38	unet_resnet34.pth
U-Net	SegFormer	47.28	82.67%	47.3M	8.7GB	52	unet_segformer.pth

Per-Class Performance (DeepLabV3+ EfficientNet-B2)

Class ID	Land Cover	IoU	F1-Score	Precision	Recall	Area Coverage
1	Water Bodies	37.51	54.55	61.2%	49.8%	18.7%
2	Dense Forest	40.03	57.15	78.9%	45.1%	8.2%
3	Built up	48.52	65.31	69.4%	61.7%	15.4%
4	Agriculture land	53.53	69.84	72.1%	67.8%	28.9%
5	Barren land	69.31	81.97	85.3%	78.9%	3.1%
6	Fallow land	88.03	93.63	94.7%	92.6%	21.8%
7	Sparse Forest	50.27	66.89	71.4%	62.9%	1.6%

🏗️ Model Zoo

Pre-trained models available in the models/ directory:

Production Models

Model	Use Case	Accuracy	Speed	Size	Model File
DeepLabV3+ Server	High accuracy research	mIoU: 55.31	45ms	32MB	deeplabv3_effecientnet_b2.pth
U-Net ResNet34	Balanced performance	mIoU: 46.12	38ms	97MB	unet_resnet34.pth
U-Net SegFormer	Transformer-based	mIoU: 47.28	52ms	189MB	unet_segformer.pth

Dataset Structure

The framework expects the following dataset structure (as created by data_preprocessing.py):

SEN-2_LULC_preprocessed/
├── train_images/          # Training satellite images
├── train_masks/           # Training segmentation masks  
├── val_images/            # Validation satellite images
└── val_masks/             # Validation segmentation masks

🔄 Execution Results Preview

🌍 Applications & Use Cases

Environmental Monitoring

• Deforestation tracking with temporal analysis using satellite time series
• Urban expansion monitoring for sustainable city planning and development
• Agricultural land assessment for food security and crop yield prediction
• Water body changes monitoring due to climate variations and human impact

Government & Policy Applications

• Land use compliance monitoring for regulatory enforcement
• Environmental impact assessment for infrastructure projects
• Disaster response and damage assessment using before/after imagery
• Carbon footprint analysis and emissions reporting

Commercial & Research Applications

• Real estate development site suitability analysis
• Insurance risk assessment for natural disasters and climate risks
• Precision agriculture for optimized farming and resource management
• Infrastructure planning and optimal site selection for renewable energy

🌟 Star History

📄 License

This project is released under the MIT License.

Acknowledgments: This work was supported by [Your Institution/Grant]. Special thanks to the open-source community and contributors to PyTorch, segmentation-models-pytorch, and the geospatial data science ecosystem.

For support, questions, or collaboration opportunities:

• 📧 Email: vishalpainjane22@gmail.com
• 💬 GitHub Discussions: Join the community
• 🌐 Web Demo: Try the live application