{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QGWZGIYBFREWOZEUCHK4LEMSR7","short_pith_number":"pith:QGWZGIYB","schema_version":"1.0","canonical_sha256":"81ad9323012c4967649411d5c591928fe04143437369cacd3dc04893fb072d2c","source":{"kind":"arxiv","id":"2303.04506","version":2},"attestation_state":"computed","paper":{"title":"Radio astronomical images object detection and segmentation: A benchmark on deep learning methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea DeMarco, Andrew M. Hopkins, Carmelo Pino, Concetto Spampinato, Cristobal Bordiu, Daniel Magro, Eva Sciacca, Filomena Bufano, Francesco Schillir\\`o, Giuseppe Fiameni, Renato Sortino, Simone Riggi","submitted_at":"2023-03-08T10:55:24Z","abstract_excerpt":"In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical"},"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":"2303.04506","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-08T10:55:24Z","cross_cats_sorted":[],"title_canon_sha256":"cd88e10870dca748c02d2356cf28205596bd328db72aac2f9402b6cb15a644b2","abstract_canon_sha256":"b424dcfd21aa43e34ca11842f109f8975a53020778cb0aed474575abe2850bb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:55.386155Z","signature_b64":"7WtLf6tJWa6ol+4YYPrSWYfck0IjI5ufVQNXIWk7i4w9y9piMs5zTZj5yIeRhGNrAjEPgGkA5SEc1jjGK2TsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81ad9323012c4967649411d5c591928fe04143437369cacd3dc04893fb072d2c","last_reissued_at":"2026-07-05T06:13:55.385650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:55.385650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Radio astronomical images object detection and segmentation: A benchmark on deep learning methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea DeMarco, Andrew M. Hopkins, Carmelo Pino, Concetto Spampinato, Cristobal Bordiu, Daniel Magro, Eva Sciacca, Filomena Bufano, Francesco Schillir\\`o, Giuseppe Fiameni, Renato Sortino, Simone Riggi","submitted_at":"2023-03-08T10:55:24Z","abstract_excerpt":"In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04506","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/2303.04506/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":"2303.04506","created_at":"2026-07-05T06:13:55.385709+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04506v2","created_at":"2026-07-05T06:13:55.385709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04506","created_at":"2026-07-05T06:13:55.385709+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGWZGIYBFREW","created_at":"2026-07-05T06:13:55.385709+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGWZGIYBFREWOZEU","created_at":"2026-07-05T06:13:55.385709+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGWZGIYB","created_at":"2026-07-05T06:13:55.385709+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/QGWZGIYBFREWOZEUCHK4LEMSR7","json":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7.json","graph_json":"https://pith.science/api/pith-number/QGWZGIYBFREWOZEUCHK4LEMSR7/graph.json","events_json":"https://pith.science/api/pith-number/QGWZGIYBFREWOZEUCHK4LEMSR7/events.json","paper":"https://pith.science/paper/QGWZGIYB"},"agent_actions":{"view_html":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7","download_json":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7.json","view_paper":"https://pith.science/paper/QGWZGIYB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04506&json=true","fetch_graph":"https://pith.science/api/pith-number/QGWZGIYBFREWOZEUCHK4LEMSR7/graph.json","fetch_events":"https://pith.science/api/pith-number/QGWZGIYBFREWOZEUCHK4LEMSR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7/action/storage_attestation","attest_author":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7/action/author_attestation","sign_citation":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7/action/citation_signature","submit_replication":"https://pith.science/pith/QGWZGIYBFREWOZEUCHK4LEMSR7/action/replication_record"}},"created_at":"2026-07-05T06:13:55.385709+00:00","updated_at":"2026-07-05T06:13:55.385709+00:00"}