Identify composite vs natural using AI

Below is a free classifier to identify composite vs natural. Just upload your image, and our AI will predict if the image is a composite or natural scene - in just seconds.

API Access


import nyckel

credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("composite-vs-natural", "your_image_url", credentials)


fetch('https://www.nyckel.com/v1/functions/composite-vs-natural/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/composite-vs-natural/invoke

How this classifier works

To start, upload your image. Our AI tool will then predict if the image is a composite or natural scene.

This pretrained image model uses a Nyckel-created dataset and has 2 labels, including Composite and Natural.

We'll also show a confidence score (the higher the number, the more confident the AI model is around if the image is a composite or natural scene).

Whether you're just curious or building composite vs natural detection into your application, we hope our classifier proves helpful.

Need to identify composite vs natural at scale?

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

Environmental Monitoring: This function can be used to analyze satellite images to distinguish between composite materials, such as synthetic land cover, and natural terrains. This information can help in assessing deforestation, urban development, and land use changes over time.

Agriculture Assessment: Farmers and agronomists can leverage the classification to identify areas of cultivated land versus natural vegetation. By understanding the distribution of composite versus natural land, they can optimize irrigation, crop rotation, and pest management strategies.

Urban Planning: City planners can utilize the identifier to evaluate land use in urban environments. By distinguishing between natural areas and constructed spaces, they can better plan for green spaces, zoning regulations, and environmental impact assessments.

Wildlife Conservation: Conservationists can use the classification to monitor habitats and assess the impact of human activity on natural versus developed environments. This can aid in creating strategies to protect endangered wildlife and preserve biodiversity.

Disaster Response: During natural disasters, this classification can help emergency services distinguish between affected natural ecosystems and man-made structures. This information is vital for deploying resources and planning recovery efforts effectively.

Marketing and Advertising: Businesses in tourism and eco-friendly products can use the identifier to target their marketing efforts more effectively. By understanding the blend of natural and composite environments in specific regions, they can attract customers interested in nature-based experiences.

Climate Change Research: Researchers can utilize the classification to study the effects of climate change on different land types. By analyzing shifts in composite versus natural land, they can identify trends and better predict future environmental changes related to global warming.