Image Feature Extraction
Extract and analyze visual features from satellite imagery using AI embeddings
Powered by Meta’s DINOv3! This task uses Meta’s latest DINOv3 model for self-supervised learning at unprecedented scale, providing state-of-the-art image representations for geospatial analysis.
Quick Start
import { geoai } from "geoai";
// Initialize pipeline with DINOv3 model
const pipeline = await geoai.pipeline(
[{ task: "image-feature-extraction" }],
providerParams
);
// Run feature extraction
const result = await pipeline.inference({
inputs: { polygon: myPolygon },
});
console.log(`Extracted features for ${result.embeddings.length} patches`);Uses Meta’s DINOv3 model to extract high-dimensional feature vectors from satellite imagery patches. DINOv3 provides state-of-the-art self-supervised learning for vision at unprecedented scale.
Parameters
Post-Processing
postProcessingParams: {
patchSize: 224; // Size of image patches in pixels
overlap: 0.1; // Overlap between patches (0.0-1.0)
}Map Source
mapSourceParams: {
zoomLevel: 18; // Image resolution (16-20)
}See Map Source Parameters for more details.
Use Cases
| Application | Description |
|---|---|
| Similarity Search | Find similar areas across large datasets |
| Change Detection | Identify changes between time periods |
| Land Classification | Categorize terrain types using embeddings |
| Anomaly Detection | Find unusual patterns in satellite imagery |
| Feature Matching | Match corresponding features across images |
Output
Returns embeddings for each image patch:
{
embeddings: [
{
geometry: { /* patch polygon coordinates */ },
properties: {
embedding: [0.1, 0.2, 0.3, ...], // 1024-dimensional vector
patchId: "patch_001"
}
}
]
}Coming Soon
Advanced features like similarity search and batch processing are coming soon!
Feature extraction requires more computational resources than object detection. Consider using Web Workers for better performance.