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Deep learning model detects 'dark vessels' using satellite nightlight data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a deep learning model, YOLO11, to detect fishing vessels using nighttime light satellite imagery. This dual-branch architecture, processing both panchromatic and RGB data, achieved high performance metrics including a 0.96 mAP@50. When applied to the coast of India, the model identified over 31,000 vessel instances, with a significant majority (77.3%) identified as potential "dark vessels" lacking AIS transmission. The study also revealed peak fishing activity between January and April, concentrated within 50-100 km of the coastline. AI

IMPACT Enhances maritime surveillance capabilities and provides insights into fishing patterns and regulatory compliance.

RANK_REASON Academic paper detailing a new deep learning model and its application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning model detects 'dark vessels' using satellite nightlight data

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Academic paper detailing a new deep learning model and its application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shantakar Mohanty, Prasun Kumar Gupta, Raian Vargas Maretto ·

    Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images

    arXiv:2608.09360v1 Announce Type: cross Abstract: The demand for maritime surveillance has given rise to the need for monitoring fishing vessel activities, particularly in addressing the challenge of "dark vessels" that operate without Automatic Identification System (AIS) transm…

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