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rjspark/satellite-image-captioning

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App README

🛰️ AI-Powered Satellite Image Captioning Platform

Advanced deep learning framework for automated natural language description generation from satellite imagery

Try Demo • Documentation • Technical Details

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📋 Overview

This project implements a sophisticated deep learning framework for automated caption generation of satellite imagery. By integrating Convolutional Neural Networks (CNNs) for visual feature extraction and Long Short-Term Memory (LSTM) networks for sequence generation, the system interprets complex remote sensing data to produce context-aware natural language descriptions.

The platform enables efficient large-scale image analysis and supports critical applications in environmental monitoring, urban planning, disaster response, and agricultural management.

✨ Key Features

🧠 Deep Learning Architecture

—ResNet50 Encoder: Pre-trained CNN for extracting high-level visual features from satellite imagery
—LSTM Decoder: Sequence-to-sequence model for generating natural language descriptions
—Context-Aware Processing: Understands spatial relationships and semantic content in satellite scenes

🚀 Real-Time Processing

—Optimized inference pipeline for rapid caption generation
—Supports real-time analysis for operational applications
—Efficient batch processing capabilities

🌍 Wide Application Range

—Urban Planning: Infrastructure mapping and city development monitoring
—Environmental Monitoring: Deforestation tracking, land use analysis, ecosystem health
—Disaster Response: Rapid damage assessment and infrastructure evaluation
—Agriculture: Crop classification, yield prediction, precision farming

💻 User-Friendly Interface

—Intuitive web-based platform
—Drag-and-drop image upload
—Real-time caption generation
—Responsive design for all devices

🏗️ Technical Architecture

Model Components

┌─────────────────────────────────────────────────────────┐
│                    Input Satellite Image                 │
│                        (224 × 224)                       │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                   CNN Encoder (ResNet50)                 │
│                                                          │
│  • Pre-trained on ImageNet                              │
│  • Extracts 2048-dimensional feature vectors            │
│  • Captures spatial patterns and semantic info          │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
                   [Feature Vector]
                   (Embedding: 256)
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                   LSTM Decoder Network                   │
│                                                          │
│  • Hidden Size: 512 units                               │
│  • Vocabulary-based word generation                     │
│  • Sequential caption construction                      │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│              Natural Language Description                │
│         "Urban area with dense buildings and            │
│          infrastructure network visible"                │
└─────────────────────────────────────────────────────────┘

Technology Stack

Component	Technology
Deep Learning Framework	PyTorch 2.1.0
Computer Vision	torchvision, PIL
Web Framework	Flask 3.0.0
Frontend	HTML5, CSS3, JavaScript
Deployment	Docker, Hugging Face Spaces
Model Architecture	ResNet50 + LSTM

🎯 Applications

1. Urban Planning & Development

—Automated analysis of urban growth patterns
—Infrastructure mapping and monitoring
—Smart city development support
—Transportation network analysis

2. Environmental Monitoring

—Deforestation and reforestation tracking
—Land use change detection
—Ecosystem health assessment
—Biodiversity conservation support

3. Disaster Management

—Post-disaster damage assessment
—Infrastructure integrity evaluation
—Emergency response planning
—Recovery operation support

4. Agriculture & Forestry

—Crop type classification
—Yield prediction and monitoring
—Forest management and logging detection
—Precision agriculture applications

🚀 How It Works

Step-by-Step Process

1.Image Upload: User uploads a satellite image through the web interface
2.Preprocessing: Image is resized to 224×224 and normalized
3.Feature Extraction: ResNet50 CNN extracts visual features
4.Caption Generation: LSTM decoder generates word sequence
5.Post-processing: Special tokens removed, caption formatted
6.Display: Natural language description shown to user

Technical Workflow

python

# 1. Image Preprocessing
image → resize(224, 224) → normalize() → tensor

# 2. Feature Extraction (Encoder)
visual_features = ResNet50(image_tensor)

# 3. Caption Generation (Decoder)
caption_ids = LSTM_Decoder.sample(visual_features)

# 4. Vocabulary Mapping
words = [vocab.itos[id] for id in caption_ids]

# 5. Output Generation
caption = " ".join(words)

📊 Model Performance

Architecture Specifications

Parameter	Value
Encoder	ResNet50 (Pre-trained)
Embedding Dimension	256
LSTM Hidden Units	512
LSTM Layers	1
Vocabulary Size	Custom (trained on dataset)
Max Sequence Length	20 words
Input Image Size	224 × 224 pixels

Processing Capabilities

—Inference Speed: ~2-3 seconds per image (CPU)
—Supported Formats: JPG, PNG, JPEG
—Max File Size: 16MB
—Batch Processing: Supported
—Device Support: CPU and CUDA-enabled GPUs

🛠️ Installation & Setup

Prerequisites

bash

Python 3.9+
PyTorch 2.1.0
Flask 3.0.0

Local Deployment

1.Clone the repository

bash

git clone <repository-url>
cd satellite-caption-app

1.Install dependencies

bash

pip install -r requirements.txt

1.Place model files

- encoder.pth (trained encoder weights)
- decoder.pth (trained decoder weights)
- vocab.pkl (vocabulary object)

1.Run the application

bash

python app.py

1.Access the platform

Open browser: http://localhost:7860

Docker Deployment

bash

docker build -t satcaption-ai .
docker run -p 7860:7860 satcaption-ai

📖 Usage Guide

Web Interface

1.Navigate to the application URL
2.Click the upload area or drag and drop a satellite image
3.Wait for preview to load
4.Click "Generate Caption with AI"
5.View the generated natural language description

API Endpoint

python

POST /predict
Content-Type: multipart/form-data

# Request
{
  "file": <satellite_image_file>
}

# Response
{
  "caption": "Generated description of satellite image",
  "success": true
}

Health Check

python

GET /health

# Response
{
  "status": "healthy",
  "model": "ResNet50-LSTM",
  "device": "cuda",
  "vocab_size": 5000
}

🔬 Research & Development

Deep Learning Methodology

Encoder Architecture:

—Pre-trained ResNet50 provides transfer learning benefits
—Removes final classification layer for feature extraction
—Adds linear projection to embedding space
—Batch normalization for stable training

Decoder Architecture:

—LSTM cells maintain long-term dependencies
—Attention mechanism potential for future enhancement
—Beam search capability for improved caption quality
—Temperature-based sampling for diversity control

Training Process

python

# Encoder: Feature extraction
features = CNN_Encoder(images)

# Decoder: Caption generation
outputs = LSTM_Decoder(features, captions)

# Loss calculation
loss = CrossEntropyLoss(outputs, targets)

# Optimization
optimizer.step()

📈 Future Enhancements

Planned Features

—[ ] Attention Mechanism: Improve focus on relevant image regions
—[ ] Beam Search: Generate multiple caption candidates
—[ ] Multi-Language Support: Captions in multiple languages
—[ ] Confidence Scores: Probability metrics for generated captions
—[ ] Batch Processing: Upload and process multiple images
—[ ] Fine-tuning Interface: Allow users to train on custom datasets
—[ ] API Integration: RESTful API for third-party applications
—[ ] Mobile Application: Native iOS and Android apps

Model Improvements

—Transformer-based architecture (Vision Transformer + GPT)
—Larger vocabulary for more diverse descriptions
—Domain-specific fine-tuning (urban, agricultural, coastal)
—Multi-modal learning with additional data sources

🤝 Contributing

Contributions are welcome! Areas of interest:

—Model architecture improvements
—Dataset expansion
—UI/UX enhancements
—Documentation improvements
—Bug fixes and optimization

📄 License

This project is developed for educational and research purposes.

🙏 Acknowledgments

—PyTorch Team: Deep learning framework
—ResNet Architecture: Original paper by He et al.
—LSTM Networks: Hochreiter & Schmidhuber
—Satellite Imagery: Remote sensing data providers

📧 Contact & Support

For questions, issues, or collaboration opportunities:

—🐛 Issues: GitHub Issues
—📧 Email: your.email@example.com
—💼 LinkedIn: Your Profile
—🌐 Portfolio: Your Website

📊 Project Statistics

📦 Lines of Code: ~1,500
🧠 Model Parameters: ~25M (ResNet50) + 2M (LSTM)
💾 Model Size: ~100MB
⚡ Inference Time: 2-3 seconds (CPU)
🎯 Application Domain: Remote Sensing & Computer Vision

🌟 Star History

If you find this project useful, please consider giving it a star! ⭐

Built with ❤️ using PyTorch and Deep Learning

🛰️ Enabling intelligent interpretation of our planet from space 🌍

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