Current state of greenery in record time
Roadside.vision identifies and assesses trees using remote sensing (camera/lidar on a car) or an existing database of panoramic road images (similar to Google StreetView). The analysis is handled quickly and reliably by AI thanks to machine learning. This is a very fast solution, and a bonus is a transparent price per km of road, not per number of trees.
- Easy work planning for greenery management
- Timely care for high-risk trees for road management
- Affordable and fast greenery inventory for cities
We locate and identify individual trees near the road with almost 100% certainty; in municipalities, our success rate is 90%.
We bring nature closer to people through technology.
kindwise (formerly FlowerChecker, founded in 2014) uses both human expertise and machine learning to develop a range of nature-focused products:
- plant.id
- plant.health
- insect.id
- mushroom.id
- crop.health
- plant.sky
- forestum.ai
- vegetation.mapping
How it works
High-quality data is required for very accurate and detailed analysis:
- Precisely defined area for analysis
- Driver and car
- Precise GPS
- High-resolution camera, mounted on a car
Do you only need a basic overview and photo documentation for pricing arboricultural work, etc.? We use a panoramic image database (similar to Google StreetView). Although such data is older and of lower quality, it is sufficient for basic analyses. The advantage is a lower price.
What information do you get with roadside.vision?
Detection of existence
- Freestanding trees
- Built-up area (municipality) – 91% sensitivity, 16% error rate
- Outside built-up area (outside municipality) – 99% sensitivity, 1% error rate
- Groups of trees and continuous stands
Location
(average error +-20cm)
- Location in space, where exactly the tree is located
- Distance from the edge of the road
Genus and tree parameters
(average error +- 15%)
- TOP 3 genus predictions
- Tree height
- Trunk diameter
- Trunk circumference
- Crown diameter, wood volume upon request
Indication of health and safety
(80% accuracy)
- Vitality – 65% sensitivity
- Health – 60% sensitivity
- Stability – 20% sensitivity
- Safety – 20% sensitivity
