Land Use KNN Classifier
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Classifies land use in satellite imagery using a K-Nearest Neighbors approach.
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About This Workflow
Overview
This workflow utilizes a K-Nearest Neighbors (KNN) algorithm to classify land use types within satellite imagery. It leverages an image embedding model to convert images into numerical representations, which are then used to query a Qdrant vector database. The results are processed to determine the most likely land use category.
Key Features
- Embeds images for vector representation.
- Queries a vector database (Qdrant) for similar land use types.
- Implements a majority vote mechanism to determine the final classification.
- Handles tie-breaking scenarios for classification.
How To Use
- Configure Credentials: Set up HTTP credentials for the Voyage API and Qdrant.
- Set Environment Variables: Define
API_BASE_URLfor the image embedding service andWEBHOOK_URLfor Qdrant. - Input Image URL: Provide the URL of the satellite image to be classified.
- Run Workflow: Execute the workflow. The output will contain the predicted land use category.
Apps Used
Workflow JSON
{
"id": "0fb8838f-ea1a-4041-9a0b-0a592c6f3dcc",
"name": "Land Use KNN Classifier",
"nodes": 0,
"category": "AI/Machine Learning",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 0fb8838f-ea1a...
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