amos3
softwarePython 3 client for the Archive of Many Outdoor Scenes (AMOS), enabling access to billions of outdoor webcam images for computer vision and environmental research
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A modern Python 3 client library for accessing the Archive of Many Outdoor Scenes (AMOS), one of the world’s largest collections of outdoor webcam imagery containing over 1 billion images from nearly 30,000 cameras worldwide.
Project Overview
amos3 provides a clean, efficient interface to the massive AMOS dataset maintained by Washington University’s Media and Machines Lab. The library enables researchers and developers to programmatically access years of timelapse imagery for computer vision, environmental monitoring, and urban planning applications.
Key Features
Data Access
- Camera Management: Retrieve lists and metadata for thousands of cameras
- Image Retrieval: Download individual images by camera and timestamp
- Bulk Downloads: Access monthly ZIP archives for efficient data transfer
- Dataset Construction: Build custom datasets with specific cameras and timeframes
Storage Options
- Local Storage: Save datasets to local filesystem
- AWS S3 Integration: Direct upload to S3 buckets for cloud-based workflows
- Efficient Caching: Smart caching to minimize redundant downloads
Technical Implementation
The library includes:
- Asynchronous download capabilities
- Robust error handling for network issues
- Comprehensive unit test coverage
- Clean Python 3 API design
- Metadata management system
AMOS Dataset
The Archive of Many Outdoor Scenes represents:
- Scale: 1+ billion images from ~30,000 cameras
- Duration: 15+ years of continuous capture
- Coverage: Global locations with emphasis on North America
- Applications: Environmental monitoring, urban planning, computer vision research
Use Cases
amos3 enables:
- Long-term environmental change detection
- Urban development analysis
- Weather pattern visualization
- Computer vision dataset creation
- Time-lapse photography projects
Future Development
Planned enhancements include:
- PIL/Pillow integration for image processing
- Machine learning dataset generators
- Advanced filtering and search capabilities
- Performance optimizations for large-scale downloads
Licensed under MIT to support open research in computer vision and environmental science.