AI-Powered Property Photo Classification Implementation Guide

Overview

Our AI-powered property photo classification feature automatically identifies and categorizes real estate listing photos, making it easier for users to browse and organize property images. This technology analyzes photos and assigns them classifications like "Kitchen," "Bathroom," "Living Room," and more.

How It Works

The classification system uses machine learning to analyze property photos and predict what room or area each image shows. Each classification includes:

• Room/Area Type: The predicted classification (e.g., "Kitchen", "Bedroom")
• Confidence Score: A prediction value between 0 and 1 indicating how confident the AI is in its classification

API Integration

Classifications are included in the imageInsights object within your API response. Here's the structure:

"images": [
    {
        "image": "IMG-C12134349_13.jpg",
        "classification": {
            "imageOf": "Kitchen",
            "prediction": 0.9993257523
        }
    },
    {
        "image": "IMG-C12134349_22.jpg",
        "classification": {
            "imageOf": "Bedroom",
            "prediction": 0.964843154
        }
    }
]

Supported Classifications

Our system currently identifies these room and area types:

• Aerial View
• Back of Structure
• Balcony
• Basement
• Bathroom
• Bedroom
• Dining Room
• Entrance Foyer
• Exercise Room
• Family Room
• Floor Plan
• Front of Structure
• Game Room
• Garage
• Hallway
• Kitchen
• Laundry
• Living Room
• Lobby
• Office
• Other
• Parking
• Patio
• Pool
• Side of Structure
• Stairs
• View
• Walk-In Closet(s)
• Wine Cellar

Implementation Best Practices

Confidence Thresholds

The prediction value indicates classification confidence. Consider these guidelines:

• High Confidence (0.95+): Very reliable classifications suitable for automatic labeling
• Good Confidence (0.90-0.94): Generally reliable, good for most use cases
• Lower Confidence (<0.90): Use with caution or exclude from automatic grouping

Recommendation: Set a minimum threshold of 0.90 for displaying classifications to users.

User Experience Applications

Image Labeling

// Display classification labels for high-confidence predictions
if (image.classification.prediction >= 0.90) {
    displayLabel(image.classification.imageOf);
}

Image Grouping and Filtering

// Group images by room type for easier browsing
const kitchenImages = images.filter(img => 
    img.classification.imageOf === "Kitchen" && 
    img.classification.prediction >= 0.90
);

Agent Workflow Enhancement

• Pre-populate MLS upload forms with suggested classifications
• Help agents organize photos before listing submission
• Reduce manual categorization time

Error Handling

Always include fallback handling for images without classifications:

// Handle missing or low-confidence classifications
const getDisplayLabel = (image) => {
    if (!image.classification || image.classification.prediction < 0.90) {
        return "Property Photo";
    }
    return image.classification.imageOf;
};

Common Use Cases

Photo Gallery Enhancement

Create tabbed interfaces where users can view photos by room type:

• "All Photos" (default view)
• "Kitchen" (filtered to kitchen images only)
• "Bedrooms" (filtered to bedroom images)
• "Bathrooms" (filtered to bathroom images)

Listing Management Tools

Help real estate professionals organize their photo uploads:

• Automatic photo sorting during MLS submission
• Bulk classification suggestions
• Quality control workflows

Search and Discovery

Enable room-specific search capabilities:

• "Show me all kitchens in this price range"
• "Compare master bathrooms across similar properties"

Testing and Validation

When implementing photo classification:

1. Test with diverse property types - Ensure accuracy across different architectural styles
2. Monitor confidence scores - Track prediction accuracy over time
3. Gather user feedback - Allow users to correct misclassifications
4. Handle edge cases - Some photos may not fit standard categories

Support and Troubleshooting

Common Issues:

• Missing classifications: Some images may not have classification data if processing failed
• Unexpected labels: Handle unknown classification types by displaying generic labels
• Low confidence scores: Filter out predictions below your chosen threshold

For technical support or questions about implementing photo classification features, contact our developer support team.

Updated on: 26/05/2025