{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IXTTD3CKMV5Q2JUDMSAHVYWHBW","short_pith_number":"pith:IXTTD3CK","schema_version":"1.0","canonical_sha256":"45e731ec4a657b0d268364807ae2c70d97c64c88880f1a5880fda18ee3b67a14","source":{"kind":"arxiv","id":"2312.03207","version":2},"attestation_state":"computed","paper":{"title":"Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Favyen Bastani, Henry Herzog, Joe Ferdinando, Patrick Beukema, Piper Wolters, Yawen Zheng","submitted_at":"2023-12-06T00:48:50Z","abstract_excerpt":"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)"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.03207","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T00:48:50Z","cross_cats_sorted":[],"title_canon_sha256":"aefcfe746290622bfd668d3faf9415e9fd9e92c3b89fee5e788a37e7d566c56c","abstract_canon_sha256":"22b7604abaf1131cce0a3f74247ac23ee47c14004035f80b2c6deea01758b3f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:10.803009Z","signature_b64":"emJTS5pRhIFB8UIkVgY1SqLpE4PAF9z2YzXICOufWaMvcBOt63F5P3+VzF6JwCSj1BrgqDAg78lJ6Us9/45WBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45e731ec4a657b0d268364807ae2c70d97c64c88880f1a5880fda18ee3b67a14","last_reissued_at":"2026-07-05T11:12:10.802526Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:10.802526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact (Version. 2.0)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Favyen Bastani, Henry Herzog, Joe Ferdinando, Patrick Beukema, Piper Wolters, Yawen Zheng","submitted_at":"2023-12-06T00:48:50Z","abstract_excerpt":"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)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03207","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.03207/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.03207","created_at":"2026-07-05T11:12:10.802580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.03207v2","created_at":"2026-07-05T11:12:10.802580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03207","created_at":"2026-07-05T11:12:10.802580+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXTTD3CKMV5Q","created_at":"2026-07-05T11:12:10.802580+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXTTD3CKMV5Q2JUD","created_at":"2026-07-05T11:12:10.802580+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXTTD3CK","created_at":"2026-07-05T11:12:10.802580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.00858","citing_title":"Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW","json":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW.json","graph_json":"https://pith.science/api/pith-number/IXTTD3CKMV5Q2JUDMSAHVYWHBW/graph.json","events_json":"https://pith.science/api/pith-number/IXTTD3CKMV5Q2JUDMSAHVYWHBW/events.json","paper":"https://pith.science/paper/IXTTD3CK"},"agent_actions":{"view_html":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW","download_json":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW.json","view_paper":"https://pith.science/paper/IXTTD3CK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.03207&json=true","fetch_graph":"https://pith.science/api/pith-number/IXTTD3CKMV5Q2JUDMSAHVYWHBW/graph.json","fetch_events":"https://pith.science/api/pith-number/IXTTD3CKMV5Q2JUDMSAHVYWHBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW/action/storage_attestation","attest_author":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW/action/author_attestation","sign_citation":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW/action/citation_signature","submit_replication":"https://pith.science/pith/IXTTD3CKMV5Q2JUDMSAHVYWHBW/action/replication_record"}},"created_at":"2026-07-05T11:12:10.802580+00:00","updated_at":"2026-07-05T11:12:10.802580+00:00"}