OceanSense-Fish is a real-time analytics platform that visualizes fish migration patterns and predicts ocean productivity using environmental parameters like Sea Surface Temperature (SST), Sea Surface Salinity (SSS), and Chlorophyll-a (CHL) anomalies.
OceanSense-Fish is a real-time marine telemetry and fisheries migration dashboard built using Streamlit and FastAPI.
It simulates and visualizes live fish movement and productivity trends through WebSocket-based data streaming and geospatial visualization.
This project demonstrates how real-time data pipelines can be used to monitor and visualize fisheries-related datasets in an interactive dashboard.
It connects a backend simulation engine (via WebSockets) with a Streamlit-powered frontend dashboard that displays live updates on fish telemetry.
The broader goal of OceanSense is to integrate open marine biodiversity datasets (like OBIS and NOAA ERDDAP) to make fish migration and productivity insights more accessible for research and conservation.
- 📡 Real-time data streaming via WebSocket (FastAPI backend → Streamlit frontend)
- 🗺️ Interactive geospatial visualization using Plotly Mapbox
- 📊 Live telemetry feed for fish movement (ID, latitude, longitude, speed, heading, timestamp)
- 📈 Data table view with auto-updating telemetry records
- ✅ Connection status & last update indicators
- 💾 Optional processed data download functionality (coming soon)
| Component | Technology |
|---|---|
| Backend | FastAPI + WebSocket |
| Frontend | Streamlit + Plotly Express |
| Data | OBIS / NOAA ERDDAP (open ocean datasets) |
| Language | Python 3.10+ |
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Clone the repository
git clone https://github.com/<your-username>/oceansense-fish.git cd oceansense-fish
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Create a virtual environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install dependencies
pip install -r requirements.txt
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Start the backend
cd backend python main.py -
Run the dashboard
streamlit run dashboard.py
- Integrate live OBIS/NOAA ERDDAP APIs for real-time fishery and productivity data.
- Add machine learning models to predict migration routes and productivity hotspots.
- Create download/export options for processed or derived datasets.
- Host on Streamlit Cloud or Render for public access.
This project uses publicly available data from:
- OBIS - Ocean Biodiversity Information System
- NOAA ERDDAP - Environmental Research Division’s Data Access Program
Data and services are made freely available under their respective open data policies.
Users are encouraged to cite the original data sources when reusing information.
Abhidyu Ajila
📸 Wildlife Photographer & Marine Biology Student
🐾 Passionate about marine ecosystems, sustainability, and conservation storytelling.
🌐 Instagram: @bearded_tarzaan
This project is open-source under the MIT License.