28 lines
709 B
Markdown
28 lines
709 B
Markdown
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# Flood Risk Monitoring
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VIMS instance for flood-monitoring.
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## Related Article
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[/insights/the-future-of-infrastructure-monitoring/](https://landvex.com/insights/the-future-of-infrastructure-monitoring/)
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## Anomaly Classes
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- water_level_rise
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- drainage_blockage
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- levee_damage
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- erosion
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- debris_accumulation
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## Quick Start
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1. Add training images to `data/raw/`
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2. Annotate using LabelImg (YOLO format)
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3. Run preprocessing: `python src/detector.py`
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4. Train model: `python src/detector.py --train`
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5. Run inference: `python src/detector.py --predict data/test/image.jpg`
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## API
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Once deployed, access via:
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- REST: `POST /api/flood-monitoring/predict`
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- WebSocket: `ws://host/ws/flood-monitoring/alerts`
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