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SWEEP Workflows

Welcome to the SWEEP Workflows repository by DotMote Labs, showcasing executable examples and workflow templates for use with the SWEEP platform — a cloud-native engine for geospatial and IoT data processing.

This repository contains demo workflows, task scripts, and Jupyter notebooks to help you prototype, test, and deploy automated pipelines on SWEEP.

🌐 What is SWEEP?

SWEEP (Scalable Workflow Execution & Event Processing) is a serverless, graph-based engine that supports sensor-to-cloud automation for:

  • Remote sensing pipelines
  • IoT network integration
  • Environmental & ecological monitoring
  • Geospatial ML inference workflows

Workflows are defined declaratively as DAGs (Directed Acyclic Graphs), with each node performing a task and routing data to the next stage.

📁 Repository Structure

SWEEP-Workflows/
├── demo-workflows/
│   ├── landsat_ndvi/(coming)
│   ├── crop_health_monitoring/(coming)
│   ├── soil_moisture_alerts/(coming)
│   ├── coffee-berries
│   ├── meadows-demo
│   └── README.md (workflow-specific instructions)
├── notebooks/
│   ├── visualize_ndvi.ipynb
│   └── calibrate_sensors.ipynb
├── tasks/
│   ├── download_satellite.py
│   ├── compute_ndvi.py
│   └── push_to_s3.py
└── LICENSE

Each demo-workflows/ subfolder contains a complete, runnable DAG definition in SWEEP config JSON format, plus task references.

🚀 Getting Started

1. Clone the Repo

git clone https://github.com/SWEEP-Inc/SWEEP-Workflows.git
cd SWEEP-Workflows

2. Set Up Your Environment

Install Python dependencies if your task modules use any:

pip install -r requirements.txt  # optional

3. Run on SWEEP

To launch a workflow:

  1. Log into your SWEEP dashboard
  2. Navigate to Workflows → Import
  3. Upload the JSON file from demo-workflows/your-workflow/
  4. Customize parameters as needed
  5. Execute and monitor via the UI

🧠 Example Use Cases

  • Landsat NDVI Computation: Triggered on new satellite data, calculates NDVI and sends alerts if vegetation health drops.
  • Soil Moisture Monitoring: Pulls IoT sensor readings and alerts when moisture falls below thresholds.
  • Sensor Calibration: Uses Jupyter notebooks to visualize and tune field sensor response curves.

📚 Learn More

🤝 Contributing

We welcome contributions — whether it's a new example workflow, bugfix, or integration. Open an issue or submit a pull request.

🪪 License

This project is licensed under the MIT License.