MISSION 004
SAR-Based Maritime Surveillance for Dark Vessel Detection and Forecasting
An AI-based maritime surveillance system that uses Sentinel-1 SAR imagery, YOLOv8, and AIS data fusion to detect dark vessels, classify ship types, and forecast vessel trajectories in the Bay of Bengal.
STATUS: RESEARCH / DEVELOPMENT
TECH STACK
- Python
- PyTorch
- YOLOv8
- TensorFlow
- OpenCV
- Rasterio
- GeoPandas
- GDAL
- Shapely
- Streamlit
- FastAPI
- Folium / Leaflet
- Sentinel-1 SAR
- AIS Fusion
- LSTM
- Kalman Filter
MISSION BRIEF
Maritime surveillance plays a critical role in coastal security, fisheries regulation, illegal vessel detection, and search-and-rescue operations. A major challenge in ocean monitoring is the presence of dark vessels, which intentionally disable their Automatic Identification System (AIS) to avoid detection.
Conventional optical satellite imagery is often ineffective during nighttime or cloudy weather. Sentinel-1 Synthetic Aperture Radar (SAR) imagery provides reliable all-weather, day-and-night ocean monitoring instead.
PIPELINE
- 01Sentinel-1 SAR ingestion
- 02Preprocessing and tiling
- 03YOLOv8 vessel detection
- 04Ship classification
- 05AIS matching
- 06Dark vessel identification
- 07Trajectory forecasting
- 08Geofence violation checks
- 09Dashboard visualisation
- 10Alert generation
RESULTS
Region of Interest
Bay of Bengal
Chennai / Tamil Nadu coast
Primary Dataset
xView3 SAR
VV + VH bands
Forecast Horizon
30-60
minutes ahead