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

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arxiv 2312.03207 v2 pith:IXTTD3CK submitted 2023-12-06 cs.CV

classification cs.CV
keywords modelssatelliteimagerycomputerconservationdatamaritimeocean
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

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    A single multimodal transformer, Galileo, jointly learns global and local features from optical, radar, elevation, weather, and land-cover inputs and outperforms specialized models on eleven benchmarks.

  2. Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A structured protocol for deploying geospatial foundation models is introduced and validated in WorldCereal, where fine-tuned Presto outperforms a fully-supervised CatBoost baseline in crop mapping.

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