Identify width of bike lane in feet
using AI
Below is a free classifier to identify width of bike lane in feet. Just upload your image, and our AI will predict the width of bike lane in feet - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("width-of-bike-lane-in-feet", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/width-of-bike-lane-in-feet/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/width-of-bike-lane-in-feet/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the width of bike lane in feet.
This pretrained image model uses a Nyckel-created dataset and has 51 labels, including 1 Foot, 10 Feet, 11 Feet, 12 Feet, 13 Feet, 14 Feet, 15 Feet, 16 Feet, 17 Feet and 18 Feet.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the width of bike lane in feet).
Whether you're just curious or building width of bike lane in feet detection into your application, we hope our classifier proves helpful.
Related Classifiers
Need to identify width of bike lane in feet at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Quality Control for Urban Infrastructure: This function can be utilized by city planners and engineers to ensure that bike lanes meet safety and design standards. By accurately identifying the width of bike lanes, stakeholders can assess compliance with regulations and make necessary adjustments to enhance cyclist safety.
- Urban Planning and Development: Real estate developers can use this function to analyze existing bike lane widths across neighborhoods. This data can inform decisions on where to invest in new developments, particularly those targeting environmentally conscious consumers who prioritize bike accessibility.
- Traffic Safety Analysis: Transportation agencies can leverage the width classification to identify areas where bike lane dimensions contribute to accidents or unsafe conditions. By understanding these metrics, they can prioritize enhancements or redesigns of bike lanes to improve overall safety for cyclists.
- Community Engagement and Advocacy: Advocacy groups can utilize this function to gather data on bike lane widths in their communities. With this information, they can push for policies and funding to expand or improve bike lane infrastructure, fostering greater cycling accessibility for residents.
- Environmental Impact Studies: Researchers studying the impact of biking on urban air quality can use this function to correlate bike lane width with cycling frequency. Understanding how infrastructure influences biking habits can provide insights into promoting sustainable transportation solutions.
- Insurance Risk Assessment: Insurance companies could analyze bike lane widths to assess risks related to cycling accidents. By identifying wider or narrower lanes, they can adjust policies or premiums for clients based on their exposure to cyclist claims.
- Smart City Analytics: Smart city platforms can integrate this function to monitor and manage bike lane usage dynamically. By classifying lane widths, urban systems can better allocate resources for maintenance and improvements in real-time, enhancing the biking experience across the city.