AIS Anomaly ViT v2 is a Vision Transformer (ViT) model fine-tuned for maritime anomaly detection using visual representations derived from Automatic Identification System (AIS) vessel tracks.
The model classifies vessel trajectory images into:
Normal Maritime Behavior
Anomalous Maritime Behavior
The objective is to support maritime domain awareness, vessel monitoring, maritime security operations, and geospatial intelligence workflows by automatically identifying suspicious vessel movement patterns from AIS-derived visualizations.
The model was trained using the AIS Anomaly v1-30 dataset.
Model Details
Property
Value
Architecture
Vision Transformer (ViT)
Base Model
google/vit-base-patch16-224
Task
Image Classification
Domain
Maritime Analytics
Input
AIS Track Images
Output
Binary Classification
Classes
Normal, Anomaly
Framework
Transformers
Training Dataset
Neo111x/ais-anomaly-v1-30
Intended Use
Primary Use Cases
Maritime anomaly detection
Vessel behavior monitoring
Maritime traffic surveillance
Illegal fishing detection support
Dark vessel activity analysis
Maritime OSINT and GEOINT workflows
Maritime security research
AIS trajectory intelligence
Example Applications
Coast guard monitoring systems
Maritime traffic management
Port security operations
Maritime threat intelligence platforms
Geospatial AI research
AIS analytics dashboards
Dataset
AIS Anomaly v1-30
The model was trained on:
Dataset:Neo111x/ais-anomaly-v1-30
The dataset contains AIS trajectory visualizations generated from vessel movement data.
Each image represents a vessel track encoded as a visual pattern suitable for computer vision models.
Classes include:
Normal vessel movement
Anomalous vessel movement
Potential anomaly categories represented in the data may include:
Unusual route deviations
Suspicious loitering
AIS gaps or interruptions
Unexpected course changes
Irregular movement behavior
Training Procedure
Preprocessing
AIS trajectories are transformed into image representations before training.
Typical preprocessing steps include:
AIS trajectory extraction
Geographic normalization
Route visualization generation
Image resizing to ViT input dimensions
Dataset balancing and augmentation
Training Configuration
Parameter
Value
Model Type
Vision Transformer
Image Size
224 × 224
Classification Type
Binary
Optimizer
AdamW
Framework
PyTorch + Transformers
Evaluation
The model was evaluated on a held-out validation split derived from the AIS Anomaly v1-30 dataset.
Evaluation metrics may include:
Accuracy
Precision
Recall
F1 Score
Confusion Matrix Analysis
Users are encouraged to perform independent evaluation on operational maritime datasets before deployment in production environments.
