Four-step optimization method for urban planning video analysis
The following workflow optimization scheme is recommended to address the inefficiency of traditional streetscape analysis:
1. Data collection norms
- Drone shooting height: 50-100 meters
- Overlap rate requirement: vertical ≥ 60%, horizontal ≥ 30%
2. Intelligent Processing Program
- Establishment of a typical element labeling library:
- Road network:
{"type":"road","attributes":["lanes","material"]} - Complex:
{"type":"building","attributes":["height","function"]}
- Road network:
- Enable batch scripting:
python batch_process.py --input_dir ./videos --config city_planning.json
3. Typical output cases
- TRAFFIC ANALYSIS: "Average weekday evening peak west-to-east lane traffic density of 68 vehicles per minute"
- Space utilization: "The average building height in the commercial area is 42.5 meters, the floor area ratio is 3.8, and the percentage of glass curtain wall is 60%"
4. Visualization of results
Integration of third-party tools (e.g. ArcGIS) to enable heat map overlay analysis.
This answer comes from the articleDescribe Anything: Open source tool for generating detailed descriptions of images and video regionsThe
May not be reproduced without permission:AI productivity tools " How to optimize the workflow of urban street video analytics?
