{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AWKMBHDM6VL56C2ELBN7V25CFO","short_pith_number":"pith:AWKMBHDM","schema_version":"1.0","canonical_sha256":"0594c09c6cf557df0b44585bfaeba22bbce700723ce81d137802db7329582a2f","source":{"kind":"arxiv","id":"2506.16318","version":2},"attestation_state":"computed","paper":{"title":"Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Carmelo Scribano, Elena Govi, Giorgia Franchini, Marko Bertogna, Paolo Bertellini, Simone Parisi","submitted_at":"2025-06-19T13:48:20Z","abstract_excerpt":"Accurate mapping of agricultural field boundaries is essential for the efficient operation of agriculture. Automatic extraction from high-resolution satellite imagery, supported by computer vision techniques, can avoid costly ground surveys. In this paper, we present a pipeline for field delineation based on the Segment Anything Model (SAM), introducing a fine-tuning strategy to adapt SAM to this task. In addition to using published datasets, we describe a method for acquiring a complementary regional dataset that covers areas beyond current sources. Extensive experiments assess segmentation a"},"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":"2506.16318","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-19T13:48:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6237f2fdca050c66b34c703a33165ad48e4b4b2eb78d03c80b9b7f8cc1e3786a","abstract_canon_sha256":"9f918475521260f61814ff41d3a837fbb6fd9809704139657c6a1d47cb7519d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:46.787274Z","signature_b64":"H7G40s+xW4INkRDgbvvCyN4QuaEfzGy3cXb5mmZUPh9mtAaUE0Zq/x7ru89R7x9if/beWJzn0F2llt1tEfuBCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0594c09c6cf557df0b44585bfaeba22bbce700723ce81d137802db7329582a2f","last_reissued_at":"2026-07-05T11:25:46.786766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:46.786766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Carmelo Scribano, Elena Govi, Giorgia Franchini, Marko Bertogna, Paolo Bertellini, Simone Parisi","submitted_at":"2025-06-19T13:48:20Z","abstract_excerpt":"Accurate mapping of agricultural field boundaries is essential for the efficient operation of agriculture. Automatic extraction from high-resolution satellite imagery, supported by computer vision techniques, can avoid costly ground surveys. In this paper, we present a pipeline for field delineation based on the Segment Anything Model (SAM), introducing a fine-tuning strategy to adapt SAM to this task. In addition to using published datasets, we describe a method for acquiring a complementary regional dataset that covers areas beyond current sources. Extensive experiments assess segmentation a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.16318","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/2506.16318/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":"2506.16318","created_at":"2026-07-05T11:25:46.786829+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.16318v2","created_at":"2026-07-05T11:25:46.786829+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.16318","created_at":"2026-07-05T11:25:46.786829+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWKMBHDM6VL5","created_at":"2026-07-05T11:25:46.786829+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWKMBHDM6VL56C2E","created_at":"2026-07-05T11:25:46.786829+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWKMBHDM","created_at":"2026-07-05T11:25:46.786829+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO","json":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO.json","graph_json":"https://pith.science/api/pith-number/AWKMBHDM6VL56C2ELBN7V25CFO/graph.json","events_json":"https://pith.science/api/pith-number/AWKMBHDM6VL56C2ELBN7V25CFO/events.json","paper":"https://pith.science/paper/AWKMBHDM"},"agent_actions":{"view_html":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO","download_json":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO.json","view_paper":"https://pith.science/paper/AWKMBHDM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.16318&json=true","fetch_graph":"https://pith.science/api/pith-number/AWKMBHDM6VL56C2ELBN7V25CFO/graph.json","fetch_events":"https://pith.science/api/pith-number/AWKMBHDM6VL56C2ELBN7V25CFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO/action/storage_attestation","attest_author":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO/action/author_attestation","sign_citation":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO/action/citation_signature","submit_replication":"https://pith.science/pith/AWKMBHDM6VL56C2ELBN7V25CFO/action/replication_record"}},"created_at":"2026-07-05T11:25:46.786829+00:00","updated_at":"2026-07-05T11:25:46.786829+00:00"}