Continuous Orbital Maritime Surveillance with Sentinel-1 SAR & Meta Multimodal AI
When distant-water fishing fleets deliberately disable their AIS transponders to harvest Illex argentinus squid along the Mile 201 Blue Hole corridor, OceanEye Sentinel 2.0 fuses orbital Sentinel-1 C-Band SAR radar with Meta SAM 3.1 sub-pixel segmentation, Muse Wonder 1.0 neuro-symbolic reasoning, and Ed25519 + SHA-256 cryptographic chain of custody.
The Four Technological Pillars of OceanEye Sentinel 2.0
Engineered from first principles: microwave radar physics, deterministic System-1 triage, Meta multimodal foundation models, and cryptographic chain of custody.
01. Sentinel-1 C-Band SAR Physics (Dual-Pol VV/VH)
C-Band microwave radar (5.405 GHz) penetrates dense Southern Ocean cloud cover and darkness. Cross-polarized VV/VH fusion suppresses Bragg sea-clutter scatter and isolates high-reflectivity dihedral steel hull returns (>+50 dB RCS).
9.12 ms · 64 TILES
02. LAYA Deterministic Triage (System 1)
Instead of burning GPU cycles on gigabytes of empty ocean, the deterministic LAYA engine computes tile-level statistical variance in sub-10ms CPU latency, discarding empty sea and forwarding only anomalous SAR chips to SAM 3.1.
03. Meta SAM 3.1 Segmentation + Neuro-Symbolic Validator
The Physical Challenge: Adaptive CA-CFAR thresholding flags radar backscatter peaks, but 6-meter Southern Atlantic wave crests trigger false alarms (sea clutter).
Our Solution: CA-CFAR detection + live sam-3.1 sub-pixel mask segmentation paired with a deterministic neuro-symbolic validator auditing muse-wonder-1.0 vs muse-spark-1.3:
04. Cryptographic Chain of Custody (Ed25519)
Every detection is sealed with a canonical SHA-256 hash and an asymmetric Ed25519 signature verifiable in-browser via WebCrypto. Test flipping a single byte in the forensic payload below to trigger immediate cryptographic rejection:
Benchmark Case Study: Dark Vessel LU RONG YU 588 (SAR-TGT-001)
Calibrated synthetic benchmark scene (seed 1982) of an unlit radar contact with AIS disabled inside the South Atlantic evaluation corridor.
Target Under Analysis: Industrial Squid Jigger LU RONG YU 588
Meta AI Multimodal Pipeline & System Architecture
End-to-end multimodal pipeline chaining calibrated synthesis (muse-image-1.0), sub-pixel radar segmentation (sam-3.1), comparative forensic reasoning (muse-wonder-1.0 vs muse-spark-1.3), and deterministic neuro-symbolic validation.
Gastón Chevarria
• Verification: RLE/PNG masks sealed with SHA-256 in
sam_masks.json.
muse-wonder-1.0 achieves 26/30 (86.7%) zero-flag pre-validator pass rate and 98.0% adherence vs 19/30 (63.3%) for muse-spark-1.3.
python3 scripts/check_integrity.py