Project V.E.D.A. - Video Engineering Data Architecture
This repository represents one component of a QA framework being developed to enable mass transit data creation and distribution for AI/ML workloads in the name of citizen science.
The Vision: While these feeds are lower quality, proper curation techniques and data engineering make them viable for meaningful model training and inference. By leveraging existing public digital infrastructure, this work aims to enable citizen scientists to drive data-driven impact in their communities.
Status: Early-stage development and validation
Generated: 2025-10-15 06:08:06 UTC
Node: Worker Node w32435
Samples: 573 videos
Cameras: 645 active
Counties: ATL, CHER, COBB, GDOT, ROCK
This dataset was generated on distributed HPC infrastructure with parallel GPU workloads.
CPU:
- Model: Intel(R) Xeon(R) w3-2435
- Cores: 16
- Architecture: x86_64
Memory:
- Total: 125Gi
- ECC:
GPU:
- Count: 1
- Model: NVIDIA RTX A4000
- VRAM: 16376 MiB
- Driver: 535.247.01
- CUDA: 12.2
output/
├── samples/ # Branded MP4 recordings (20s each)
├── provenance/ # SHA-256 hashed metadata
└── .integrity/ # Cryptographic verification chain
This dataset includes cryptographic provenance:
- Every file SHA-256 hashed
- Parent→child hash chains maintained
- Third-party verifiable via
verify.sh
Current recording configuration deployed on this node.
Camera Config: configs/cameras.json
Recording Duration: 30 seconds per sample
Quality: Best available stream
This dataset is freely available for:
- Research and analysis
- Educational purposes
- Academic publications
- Personal projects
Please attribute:
- Dataset: GDOT Traffic Camera Samples
- Architecture: Project V.E.D.A.
- Source: Georgia Department of Transportation
Note: This repository contains sample outputs. The underlying collection and processing infrastructure represents separate work.
Project V.E.D.A.
Video Engineering Data Architecture
Generated: 2025-10-15 06:08:06 UTC