https://github.com/sd0e/treetastic
https://github.com/sd0e/treetastic
Last synced: 11 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/sd0e/treetastic
- Owner: sd0e
- Created: 2024-10-19T10:21:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-19T23:06:07.000Z (over 1 year ago)
- Last Synced: 2025-06-27T13:06:17.806Z (11 months ago)
- Language: Python
- Size: 42.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# treetastic
Helps to decide where to plant trees in cities and parks based on one
or more images.
## purpose
- Noise reduction
- Air quality
- Aesthetics
- Mental health
- Community space
## workflow
1. Image (geotagged) and budget provided
- Images should look down the middle of street
2. Images passed through API to Python function
3. Images analysed and determines list of ideal pixels for new trees
- Assigned with score for improvement to area
4. Program iteratively decides which trees are best for an area
5. List of trees and images are returned to the interface
## scoring system
| Selection Criteria | Weighting |
| ------ | ------ |
| Existing trees / greenery | high |
| Types of building | medium |
| Street / pavement width | high |
| Parking spaces | medium |
| Nearby roads based on location | medium |
## tech stack
- Python (interacting with AI)
- OpenCV for image analysis
- JavaScript (API)
- React user interface
## ai
AI will be used to determine which type of trees are best for a situation and
where to plant them, based on parameters in the image such as
- appearance of area
- space available (space could be made available by removing parking spaces)
- nearby roads (based on OpenStreetMap API and image geo-tag)
- types of buildings
It will also determine which areas will benefit most from trees based on a
total budget provided for all the areas in the images.