Identify the color of a roof using AI

Below is a free classifier to identify the color of a roof. Just upload your image, and our AI will predict the color of a roof it is - in just seconds.

the color of a roof identifier

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    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("the-color-of-a-roof", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/the-color-of-a-roof/invoke', {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
            'Content-Type': 'application/json',
        },
        body: JSON.stringify(
            {"data": "your_image_url"}
        )
    })
    .then(response => response.json())
    .then(data => console.log(data));
                

    curl -X POST \
        -H "Content-Type: application/json" \
        -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
        -d '{"data": "your_image_url"}' \
        https://www.nyckel.com/v1/functions/the-color-of-a-roof/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict the color of a roof it is.

This pretrained image model uses a Nyckel-created dataset and has 21 labels, including Beige, Black, Blue, Brown, Burgundy, Charcoal, Cream, Dark Blue, Dark Gray and Gray.

We'll also show a confidence score (the higher the number, the more confident the AI model is around the color of a roof it is).

Whether you're just curious or building the color of a roof detection into your application, we hope our classifier proves helpful.

Recommended Classifiers

Need to identify the color of a roof at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Real Estate Valuation: The false image classification function can be utilized by real estate appraisal firms to assess property values based on the color of roofs in a neighborhood. Different roof colors can indicate property maintenance levels and aesthetic appeal, which influence market price. By analyzing images of roofs, appraisers can provide insightful evaluations for potential buyers and investors.

  • Insurance Risk Assessment: Insurance companies can leverage this function to identify roof colors when evaluating properties for coverage. Specific roof colors may have correlations with risk levels in specific climates, influencing claims and premiums. This can help insurers establish more accurate pricing models.

  • Urban Planning: City planners can utilize roof color classification as part of larger urban development projects. Understanding the color distribution of roofs in urban areas can help in making decisions regarding city aesthetics, zoning laws, and environmental concerns, such as heat management. This can lead to more sustainably designed urban spaces.

  • Energy Efficiency Analysis: Energy auditors and efficiency consultants can use the function to analyze the impact of roof color on building energy consumption. Dark-colored roofs can absorb more heat, leading to higher air conditioning costs, while lighter roofs can reflect sunlight. This data can guide recommendations for energy-efficient upgrades.

  • Marketing for Roofing Companies: Roofing companies can use this classification to tailor marketing strategies based on prevalent roof colors in specific neighborhoods. By targeting their advertising to styles and colors that are most popular or recently trending, they can increase customer engagement and lead generation.

  • Homeowner Association Compliance: Homeowners' associations can implement this function to ensure compliance with community standards regarding roof colors. By automatically flagging non-compliant colors from submitted property images, they can help maintain neighborhood aesthetics while reducing manual review workloads.

  • Environmental Impact Studies: Environmental organizations can incorporate roof color data in studies analyzing urban heat islands and climate change impacts. By assessing how different roof colors affect local temperatures and energy usage patterns, they can advocate for policy changes to promote reflective or energy-efficient roofing solutions.

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