{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ROSR4XZXDN3HDNFVZNBRPT2VNS","short_pith_number":"pith:ROSR4XZX","schema_version":"1.0","canonical_sha256":"8ba51e5f371b7671b4b5cb4317cf556c9fa294d38c98814404e90e30fc328565","source":{"kind":"arxiv","id":"2301.01796","version":4},"attestation_state":"computed","paper":{"title":"Recursive classification of satellite imaging time-series: An application to land cover mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Bhavya Duvvuri, Deniz Erdogmus, Edward Beighley, Haoqing Li, Helena Calatrava, Pau Closas, Ricardo Borsoi, Tales Imbiriba","submitted_at":"2023-01-04T19:36:32Z","abstract_excerpt":"Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for continuously updating classification maps in real-time remain limited, especially when training data is scarce. This paper introduces the Recursive Bayesian Classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. Three experiments are conducted using Sentinel-2 data: water mapping of the Oroville Dam i"},"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":"2301.01796","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-01-04T19:36:32Z","cross_cats_sorted":[],"title_canon_sha256":"16b6e81cc896317521661c87e3de0f1141a6af4e42337b4ce757922fc4e572dd","abstract_canon_sha256":"24651b55c9ee36a0022b794a5dfa62c90fde492fdd8ac27c9abb1dbd7ab9bba2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:27.142374Z","signature_b64":"PCW48D4bQJxbkl5RVSEp9f+HHtP5BPp0e3xlAvMSUJw3aHY4oSxRV14ERhlSSx4L5WiKhaFc8JvPEMRWsQcnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ba51e5f371b7671b4b5cb4317cf556c9fa294d38c98814404e90e30fc328565","last_reissued_at":"2026-07-05T09:04:27.141907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:27.141907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recursive classification of satellite imaging time-series: An application to land cover mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Bhavya Duvvuri, Deniz Erdogmus, Edward Beighley, Haoqing Li, Helena Calatrava, Pau Closas, Ricardo Borsoi, Tales Imbiriba","submitted_at":"2023-01-04T19:36:32Z","abstract_excerpt":"Despite the extensive body of literature focused on remote sensing applications for land cover mapping and the availability of high-resolution satellite imagery, methods for continuously updating classification maps in real-time remain limited, especially when training data is scarce. This paper introduces the Recursive Bayesian Classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. Three experiments are conducted using Sentinel-2 data: water mapping of the Oroville Dam i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.01796","kind":"arxiv","version":4},"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/2301.01796/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":"2301.01796","created_at":"2026-07-05T09:04:27.141966+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.01796v4","created_at":"2026-07-05T09:04:27.141966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.01796","created_at":"2026-07-05T09:04:27.141966+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROSR4XZXDN3H","created_at":"2026-07-05T09:04:27.141966+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROSR4XZXDN3HDNFV","created_at":"2026-07-05T09:04:27.141966+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROSR4XZX","created_at":"2026-07-05T09:04:27.141966+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/ROSR4XZXDN3HDNFVZNBRPT2VNS","json":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS.json","graph_json":"https://pith.science/api/pith-number/ROSR4XZXDN3HDNFVZNBRPT2VNS/graph.json","events_json":"https://pith.science/api/pith-number/ROSR4XZXDN3HDNFVZNBRPT2VNS/events.json","paper":"https://pith.science/paper/ROSR4XZX"},"agent_actions":{"view_html":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS","download_json":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS.json","view_paper":"https://pith.science/paper/ROSR4XZX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.01796&json=true","fetch_graph":"https://pith.science/api/pith-number/ROSR4XZXDN3HDNFVZNBRPT2VNS/graph.json","fetch_events":"https://pith.science/api/pith-number/ROSR4XZXDN3HDNFVZNBRPT2VNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS/action/storage_attestation","attest_author":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS/action/author_attestation","sign_citation":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS/action/citation_signature","submit_replication":"https://pith.science/pith/ROSR4XZXDN3HDNFVZNBRPT2VNS/action/replication_record"}},"created_at":"2026-07-05T09:04:27.141966+00:00","updated_at":"2026-07-05T09:04:27.141966+00:00"}