Ship Detection (RGB)
Deep learning model to detect ships in high-resolution satellite imagery.
Pretrained Model Outputs
Geography - US
Geography - Global
Model Overview
Input High-resolution, 3-band RGB satellite imagery with a spatial resolution of 30 centimeters, or 0.3 meters.
Output Feature layer with polygons representing the detected ship(s) in the input imagery
Applicable geographies The model is expected to work well in the United States and similar geographies.
Model architecture This model uses the MaskRCNN model architecture implemented in the ArcGIS API for Python.
Accuracy metrics This model has an average precision score of 0.683.
Training data The model has been trained on an in-house ship detection dataset.
Limitations 1. The model will work on ship lengths in the range of 12–80 m. Ships less than 12 meters long may or may not be detected. 2. Traditional boats and cargo ships may or may not be detected. 3. The model may detect false positives on land surfaces. Apply a water mask to eliminate such detections.
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