Open data, open algorithms, and edge intelligence for distributed acoustic sensing (DAS) in infrastructure.
DASmartInfra is an open-source community working to make infrastructure health monitoring more measurable, reproducible, and deployable. We connect real-world DAS observations with transparent algorithms and real-time edge computing for bridges, roads, tunnels, buildings, railways, slopes, landslides, and other civil infrastructure.
DAS can observe infrastructure continuously and at large scale, but progress is constrained by a shortage of accessible, well-described field data. Algorithms developed on isolated datasets are difficult to compare, reproduce, or transfer to operational environments.
DASmartInfra addresses that gap by building a shared foundation for:
- real and responsibly released raw or processed DAS datasets;
- reproducible algorithms with documented assumptions and evaluation metrics;
- reference benchmarks that connect sensing results to engineering questions;
- streaming and edge-computing workflows for operational monitoring; and
- collaboration among geophysicists, civil engineers, transportation researchers, data scientists, infrastructure owners, and DAS vendors.
| Area | Example goals |
|---|---|
| Transportation | Vehicle detection, classification, speed, axle response, and weight estimation |
| Roads | Surface-damage detection, pavement condition, and roadbed/subgrade monitoring |
| Bridges | Traffic response, vibration characterization, anomaly detection, and structural condition trends |
| Tunnels and rail | Train/vehicle tracking, vibration monitoring, intrusion, and condition changes |
| Buildings | Ambient vibration, occupancy/traffic response, and event-driven condition assessment |
| Slopes and landslides | Rockfall, deformation-related signals, precursors, and hazard monitoring |
| Edge intelligence | Streaming preprocessing, event detection, compression, inference, and alerting |
These are starting points, not boundaries.
DASmartInfra/
├── algorithms/ # Algorithm catalog and contribution contract
├── datasets/ # Dataset catalog (metadata and links; not large binaries)
├── edge/ # Real-time and edge-computing interfaces
├── examples/ # Reproducible end-to-end examples
├── schemas/ # Machine-readable community metadata standards
├── tools/ # Repository validation utilities
├── docs/ # Architecture, data, benchmarking, and policy guides
└── .github/ # Contribution forms, PR template, and automation
Large field data should live in a durable data repository; this Git repository stores metadata, checksums, documentation, and access links. See the data contribution guide.
You do not need to release everything at once. Valuable contributions include:
- A dataset — raw or processed DAS data with acquisition context, labels, permissions, and a stable download location.
- An algorithm — documented code for signal processing, detection, estimation, imaging, machine learning, or quality control.
- A benchmark result — a reproducible evaluation on a public dataset.
- An edge component — a streaming operator, device profile, deployment example, or performance measurement.
- Domain knowledge — annotations, engineering interpretation, tutorials, issue reports, or review.
Start with CONTRIBUTING.md. Dataset contributors should also use the dataset card template.
- Field relevance: connect outputs to real infrastructure decisions.
- Reproducibility: publish enough context to repeat and audit results.
- Interoperability: prefer documented, machine-readable formats and stable interfaces.
- Responsible data sharing: protect privacy, security, ownership, and critical-infrastructure interests.
- Honest uncertainty: report limitations, failure modes, and domain shift.
- Open participation: welcome researchers, practitioners, agencies, owners, and vendors.
The project will grow from common metadata and baseline workflows toward community datasets, shared benchmarks, and deployable edge pipelines. Planned interoperability with the broader DAS Core, DAS Bay, and future DAS Edge Computing ecosystem will be developed through open interfaces rather than hard dependencies.
See ROADMAP.md and docs/architecture.md.
Important
DASmartInfra is currently in an early testing and community-foundation stage. Repository interfaces, metadata standards, and contribution workflows are provisional and may change as they are tested with real use cases. The project is not yet validated for production or safety-critical decision-making.
Templates, sample dataset cards, reference algorithms, and reproducible end-to-end examples are under active development and are planned for upcoming releases in the near future. Early proposals, feedback, and test contributions are welcome while this foundation is being refined.
Repository code is licensed under the Apache License 2.0. Individual datasets may use different open-data licenses and must declare them in their dataset cards. Documentation is made available under CC BY 4.0 unless a file states otherwise.
If this project supports your work, cite the project using CITATION.cff and cite every dataset and algorithm you use according to its own metadata.
- Propose a dataset, algorithm, benchmark, or application through a GitHub issue.
- Open a pull request for a contribution that already follows the repository contract.
- Use GitHub Discussions for questions, ideas, field experiences, and collaboration requests once Discussions is enabled.
Infrastructure monitoring becomes more capable when field data and methods can learn from one another. DASmartInfra is the place to build that shared foundation.