AquaSat-AI is an open-source framework applying AI and big data analytics to satellite-derived maritime monitoring, with a focus on Automatic Identification System (AIS) spectrogram analysis for maritime risk management.
This project integrates deep learning, computer vision, and multi-modal data fusion to enhance vessel detection, classification, and anomaly detection in challenging maritime environments.
This repository provides a pseudo-code implementation due to the confidentiality and sensitivity of the actual work. The concepts, methodologies, and structures are inspired by real-world research but do not contain proprietary or classified information.
- ✅ Multi-Modal Learning: Combines AIS spectrograms, satellite imagery (SAR, optical), and environmental data.
- ✅ Deep Learning-Based Vessel Classification: Uses CNNs, Transformers, and ensemble learning for vessel type identification.
- ✅ Real-Time Maritime Monitoring: Supports cloud deployment for real-time tracking.
- ✅ Anomaly & Risk Detection: Identifies AIS spoofing, illegal activities, and missing MMSI data.
- ✅ Open-Source & Scalable: Modular design for flexibility and scalability.
Maritime risk management requires real-time vessel tracking, but challenges include:
- Incomplete AIS data (e.g., missing or incorrect MMSI).
- Poor visibility conditions (e.g., night, fog, storms).
- Lack of multi-source data fusion for vessel identification. AquaSat-AI addresses these challenges by integrating AIS, SAR, optical imagery, and advanced AI techniques.
💡 Multi-Modal Fusion
AquaSat-AI processes and fuses multiple data sources:
- AIS Spectrograms 🛰️ → Extract ship movement patterns.
- SAR (Sentinel-1, TerraSAR-X) 🌊 → Enhance detection in bad weather.
- Optical Imagery (Sentinel-2, PlanetScope) 📷 → Validate vessel identity.
- Environmental Data (Wind, Waves, Temperature) 🌍 → Contextualize conditions.
📊 Deep Learning Models
- Baseline: ResNet, EfficientNet, SeNet.
- Advanced Architectures: Vision Transformers, ConvNeXt.
- Ensemble Learning: Combining multiple models for robust classification.
⚙️ Training Pipeline
- Preprocessing: AIS signals → Spectrograms.
- Model Training: Train on real-world AIS & satellite datasets.
- Evaluation & Uncertainty Estimation.
- Deployment: Cloud-based real-time vessel tracking.
📦 AquaSat-AI
┣ 📂 data/ # Datasets (AIS, SAR, optical images)
┣ 📂 models/ # Deep learning models
┣ 📂 notebooks/ # Jupyter notebooks for analysis
┣ 📂 scripts/ # Training & evaluation scripts
┣ 📂 config/ # Hyperparameter configs
┣ 📝 README.md # Project documentation
┗ 📝 requirements.txt # Dependencies
git clone https://github.com/bz76wto/AquaSat-AI.git
cd AquaSat-AIpip install -r requirements.txtpython scripts/preprocess_data.pypython scripts/train_model.py --model resnet50- Accuracy: 95%+ for vessel classification on AIS spectrograms.
- Precision/Recall: Evaluated across multiple weather conditions.
- Uncertainty Estimation: Confidence scores for real-world reliability.
- Use of Ensemble Learning for higher prediction certainty.
- Validation with Optical & Radar Data to reduce false positives.
- Handling Missing MMSI Data by leveraging satellite imagery.
📍 Phase 1 (Completed)
✔️ Initial model training on AIS spectrograms.
✔️ Benchmarking ResNet, EfficientNet, SeNet.
✔️ Data preprocessing pipeline.
📍 Phase 2 (In Progress)
🚀 Integrate multi-modal fusion (SAR, optical).
🚀 Implement real-time processing pipeline.
🚀 Optimize cloud-based deployment.
📍 Phase 3 (Upcoming)
📱 Expand dataset to 1000+ vessels.
📱 Deploy AIS anomaly detection module.
📱 Integrate self-supervised learning for improved generalization.
Contributions are welcome! Please follow the guidelines:
- Fork the repo 🍔
- Create a feature branch 🔥
- Submit a pull request 🚀
MIT License. See LICENSE for details.
For questions or collaborations, contact Claire Zhang or visit the GitHub Issues.