🌊 NeroSense – Adaptive Water Intelligence System

NeroSense is a scalable, modular water intelligence platform that combines remote sensing and in-situ measurements to enable adaptive, data-driven monitoring of water quality.

The system transforms satellite observations into strategic measurement plans, guiding autonomous hardware to collect data where it matters most.

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💡 Why NeroSense?

Water monitoring today is often:

• Static (fixed sampling locations)
• Fragmented (isolated data sources)
• Inefficient (high cost, low adaptability)

NeroSense changes this by introducing:

👉 Adaptive sensing – measurements guided by data
👉 Multi-source fusion – satellite + in-situ + custom inputs
👉 Intelligent prioritization – focus on high-impact areas

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🧠 Core Concept: Strategic Sampling Engine

At the heart of NeroSense is a spatial prioritization model that determines where measurements should be taken.

It is based on three key components:

• Confidence – how reliable the satellite data is (clouds, land interference)
• Risk – intensity of the observed environmental signal (e.g. chlorophyll)
• Temporal Dynamics – how fast things are changing

These are combined into priority maps, which:

• Identify hotspots
• Guide autonomous sampling routes
• Optimize resource usage

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🌍 Use Case: Phytoplankton Monitoring (Iskar Reservoir)

For the 11th edition of the Cassini hackathon, NeroSense focuses on:

👉 Detecting and tracking phytoplankton blooms

We combine:

• Satellite-derived indicators (chlorophyll, turbidity, temperature)
• Autonomous surface robot measurements

This enables:

• Early detection of bloom formation
• Targeted validation of satellite data
• Better water management decisions

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🛰️ Data Sources

NeroSense integrates multiple Earth Observation datasets:

• Sentinel-2 → High-resolution inland water monitoring
• Sentinel-3 → Spectral accuracy for water color
• Landsat 8/9 → Long-term historical trends
• MODIS → High-frequency temporal dynamics

The platform is data-agnostic, allowing users to plug in:

• Drone imagery
• External APIs
• Custom remote sensing sources

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⚙️ Getting Started

1. Clone the repository

git clone https://github.com/your-org/nerosense.git
cd nerosense

2. Setup environment variables

Create a .env file in the root directory:

APP_NAME=NeroSense
API_VERSION=v1
DEBUG=True

DATABASE_URL=postgresql://postgres:postgres@db:5432/nerosense

GEE_SERVICE_ACCOUNT=your-service-account@your-project.iam.gserviceaccount.com
GEE_CREDENTIALS_PATH=/app/credentials/gee-key.json

SECRET_KEY=your-secret-key

3. Add Google Earth Engine credentials

Create a folder credentials/ and add your key file gee-key.json:

{
  "type": "service_account",
  "project_id": "your-project-id",
  "private_key_id": "your-private-key-id",
  "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
  "client_email": "your-service-account@your-project.iam.gserviceaccount.com",
  "client_id": "your-client-id",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token"
}

4. Run with Docker

docker compose up --build

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👥 Contributors

• Nikolay Vasilev – AI & Data Science, Backend
  GitHub | LinkedIn

• Viktoria Todorova – Data Engineering & Analytics
  GitHub | LinkedIn

• Yoan Trendafilov – Embedded Systems & Sensors
  GitHub | LinkedIn

• Nikoleta Evtimova – Automation & Robotics
  GitHub | LinkedIn

• Nikola Lozanov – Full Stack Engineering
  GitHub | LinkedIn

• Martina Dimitrova – Remote Sensing & Environmental Science

  GitHub | LinkedIn

• Elena Deleva – Water Systems Engineering

  LinkedIn

• Bojan Baidanoff – GIS & Business Strategy

  GitHub | LinkedIn