mlsanityData to 7 Oct 2026

Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0)

arXiv 2312.03207 ↗First posted 6 Dec 2023cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Illegal, unreported, and unregulated (IUU) fishing poses a global threat to ocean habitats. Publicly available satellite data offered by NASA, the European Space Agency (ESA), and the U.S. Geological Survey (USGS), provide an opportunity to actively monitor this activity. Effectively leveraging satellite data for maritime conservation requires highly reliable machine learning models operating globally with minimal latency. This paper introduces four specialized computer vision models designed for a variety of sensors including Sentinel-1 (synthetic aperture radar), Sentinel-2 (optical imagery), Landsat 8-9 (optical imagery), and Suomi-NPP/NOAA-20/NOAA-21 (nighttime lights). It also presents best practices for developing and deploying global-scale real-time satellite based computer vision. All of the models are open sourced under permissive licenses. These models have all been deployed in Skylight, a real-time maritime monitoring platform, which is provided at no cost to users worldwide.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

5 of 23 references.

References · 5

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 164
  5. 2022xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagerycited by 25

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Cited by year

Citing papers per year: 2025: 2.

2 papers.