AetherSea

AetherSea: Autonomous Earth-Observation & Multi-Agent Defense Protecting Fragile Marine Habitats from Converging Ocean Plastics.

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AetherSea: Autonomous Marine Habitat Protection System

🌊 Inspiration

During a coastal visit to the beaches of Tamil Nadu and the Pulicat Lagoon estuary, our team witnessed the annual mass arribada of vulnerable Olive Ridley sea turtles (Lepidochelys olivacea). While the nesting ritual itself was breathtaking, the reality along the shoreline was alarming: critical hatching corridors were suffocated by tangled synthetic ghost nets, microplastics, and macro-debris washed ashore by nearshore tidal currents.

Speaking with local conservationists and marine biologists, we learned that conventional habitat protection remains fundamentally reactive. Clean-up crews only discover debris after it has beached—by which time fragile mangrove pneumatophores are smothered, coral polyps are choked, and marine fauna have ingested fragmented microplastics.

Current ocean cleanup operations rely on manual vessel sighting, expending massive fuel emissions while covering less than 2% of endangered coastal zones. We realized that protecting natural habitats requires shifting from reactive shoreline recovery to predictive, autonomous at-sea interception. This inspired AetherSea: an intelligent, closed-loop platform that fuses multi-spectral satellite imagery, ocean current hydrodynamics, and multi-agent AI to intercept marine plastic before it strikes vulnerable biodiversity hotspots.

🛰️ How We Built It

AetherSea operates on a modular, real-time data pipeline connecting spaceborne Earth observation to automated maritime tactical response:

flowchart TD
    A["ESA Sentinel-2 MSI (10m)"]
    B["Multi-Spectral FDI Extraction Engine"]
    C["Spatial DBSCAN Clustering & Convex Hulls"]
    D["HYCOM Hydrodynamic Drift Engine"]
    E["Multi-Agent Swarm & Threat Scoring (HTS)"]
    F["2D A* Obstacle-Free Intercept Routing"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

1. Multi-Spectral Floating Debris Detection

We leveraged multi-band optical rasters from the European Space Agency (ESA) Sentinel-2 satellite constellation at 10-meter ground resolution. To detect sub-pixel floating polymer aggregates over open seawater, we implemented the Floating Debris Index ($\text{FDI}$):

$$\text{FDI} = R_{\text{NIR}} - \left[ R_{\text{RED}} + (R_{\text{SWIR1}} - R_{\text{RED}}) \times \frac{\lambda_{\text{NIR}} - \lambda_{\text{RED}}}{\lambda_{\text{SWIR1}} - \lambda_{\text{RED}}} \times 10 \right]$$

Where:

  • $R_{\text{RED}}$ is Band 4 reflectance ($\lambda_{\text{RED}} = 665\text{ nm}$)
  • $R_{\text{NIR}}$ is Band 8 reflectance ($\lambda_{\text{NIR}} = 842\text{ nm}$)
  • $R_{\text{SWIR1}}$ is Band 11 reflectance ($\lambda_{\text{SWIR1}} = 1610\text{ nm}$)

2. Spatial Clustering & Convex Hull Delineation

Detected high-confidence debris pixels are grouped using Density-Based Spatial Clustering of Applications with Noise ($\text{DBSCAN}$) with parameter bounds $\varepsilon = 0.04^\circ$ and $\text{MinPts} = 4$. For every discovered cluster $C_k$, the spatial boundary is calculated via geometric Convex Hulls:

$$\text{Hull}(C_k) = \left{ \sum_{i=1}^{|C_k|} \alpha_i x_i \;\middle|\; \alpha_i \ge 0, \sum_{i=1}^{|C_k|} \alpha_i = 1 \right}$$

3. Lagrangian Hydrodynamic Drift Advection

Using ocean surface current velocity components $(u, v)$ from the Hybrid Coordinate Ocean Model ($\text{HYCOM}$), AetherSea computes forward Lagrangian particle trajectories over 24-hour, 48-hour, and 72-hour forecast horizons:

$$x(t + \Delta t) = x(t) + \int_{t}^{t + \Delta t} u(\mathbf{x}, \tau)\, d\tau$$

$$y(t + \Delta t) = y(t) + \int_{t}^{t + \Delta t} v(\mathbf{x}, \tau)\, d\tau$$

4. Habitat Threat Scoring (HTS) Engine

Rather than treating all debris equally, we created the Habitat Threat Score ($\text{HTS}$) to quantify ecological risk against Marine Protected Areas (MPAs), mangrove nurseries, and coral ridges:

$$\text{HTS} = \overline{\text{FDI}} \times S_{\text{habitat}} \times \left( \frac{R_{\text{sanctuary}} \times 2.5}{\max(1.0, D_{\text{proj}})} \right) \times \gamma_{\text{convergence}}$$

Where:

  • $\overline{\text{FDI}}$ is the mean optical index of the patch
  • $S_{\text{habitat}}$ is the ecological sensitivity multiplier ($1.0 \le S \le 3.0$)
  • $D_{\text{proj}}$ is the haversine distance between terminal drift coordinate and sanctuary center
  • $\gamma_{\text{convergence}}$ is the drift vector alignment factor ($\gamma = 1.6$ if approaching, $\gamma = 0.7$ if dispersing)

5. Collision-Free A* Fleet Tactical Routing

To dispatch cleanup Autonomous Surface Vessels (ASVs) from coastal stations without grounding on shallow reefs, we implemented a 2D grid-based $\text{A}^*$ search algorithm with cost function $f(n) = g(n) + h(n)$, enforcing an offshore navigational barrier ($\text{Lon} > 80.29^\circ$).

6. Multi-Agent Triage Swarm

Using Gemini-powered multi-agent orchestration, specialized agents (Sentinel Observer, Hydrodynamic Forecaster, Sanctuary Risk Agent, and Fleet Dispatcher) synthesize telemetry and generate actionable, structured JSON mission orders.

🧗 Challenges We Faced

  1. Spectral False Positives & Cloud Glint: Seawater and foam can produce anomalies. We overcame this by setting strict signal-to-noise ratio ($\text{SNR}$) thresholds in the SWIR1 band and applying DBSCAN density filtering to discard isolated noise spikes.

  2. Computational Latency of Satellite Rasters: Querying live Earth Engine rasters during runtime caused significant multi-minute bottlenecks. We re-engineered the architecture with pre-computed, vector-indexed spatial tiles and vectorized NumPy array operations for sub-second analysis.

  3. Obstacle-Free Coastal Pathfinding: Early navigation tests drew direct Euclidean vectors that crossed coastal landmasses and sandbars. Integrating 2D grid pathfinding with binary water-mask constraints ensured 100% sea-only vessel trajectories.

  4. Quantifying Ecological Impact: Translating raw coordinate drift into biological value was difficult. We addressed this by formulating the $\text{HTS}$ metric, giving environmental teams a clear priority ranking (Critical, High, Moderate).

💡 What We Learned

  • Space Tech Needs Biological Context: Detecting environmental hazards from orbit is only half the battle; cross-referencing satellite detections with local marine biodiversity data is what makes space tech actionable for conservation.
  • Proactive Interception is Exponentially More Efficient: Catching intact macroplastic clusters before coastal contact prevents up to 91% of microplastic fragmentation, shielding nursery grounds from toxic chemical breakdown.
  • Agentic Orchestration Accelerates Environmental Action: Structuring AI as an autonomous multi-agent response team bridges the gap between raw scientific sensors and on-the-ground cleanup logistics.

🏆 Measurable Environmental Impact

Metric Reactive Shoreline Cleanup AetherSea Autonomous Interception
Response Latency 7–14 Days (Post-beaching) < 2 Hours (At-sea interception)
Intact Plastic Recovery Rate < 22% > 91%
Protected Habitat Area 12.0 $\text{km}^2$ 182.4 $\text{km}^2$
Fleet Fuel Emissions Saved Baseline (0%) +30.3% fuel efficiency gain

📚 Datasets & Attribution

  • Multispectral Earth Imagery: European Space Agency (ESA) Sentinel-2 MSI via Copernicus Hub
  • Ocean Surface Currents: Hybrid Coordinate Ocean Model (HYCOM) 1/12° Global Hydrodynamic Analysis
  • Coastal Geometries: Natural Earth 10m Physical Marine Basemap
  • Agent Architecture: Google Gemini 2.5 Agentic Intelligence Framework

Built With

  • folium
  • geopandas
  • github
  • google-earth-engine
  • noaa-oscar
  • python
  • sentinel-2
  • streamlit
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