{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KMMVOCSZOCACNVKBJTPEXJ66MC","short_pith_number":"pith:KMMVOCSZ","schema_version":"1.0","canonical_sha256":"5319570a59708026d5414cde4ba7de60aca19dc9df4bd520b351bfd7082e29a1","source":{"kind":"arxiv","id":"2410.23891","version":1},"attestation_state":"computed","paper":{"title":"AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bharath Hariharan, Cheng Perng Phoo, Chia-Hsiang Kao, Hangyu Zhou, Kavita Bala, Utkarsh Mall","submitted_at":"2024-10-31T12:52:52Z","abstract_excerpt":"Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset -- $\\textit{AllClear}$ for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical imagery from Sentinel-2 "},"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":"2410.23891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-31T12:52:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f5ed4e6b4fec7d53b90602d5d4f3ee3afcfbeaef7a14c3ef6505df683a720c6f","abstract_canon_sha256":"7e3e7155eaf5fec89c66aa6ac15a1a37edde525df8ffabccdb1eee91e6a46ee1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:06.913926Z","signature_b64":"pscjduYk+wlAhN7yPebe8B6k3opd1FGt/1vO3p4McKp6XWzWwe/nlRNCRl+8CAJ4OtcmQRsCn1ptuzufy6+tCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5319570a59708026d5414cde4ba7de60aca19dc9df4bd520b351bfd7082e29a1","last_reissued_at":"2026-07-05T09:29:06.913450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:06.913450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bharath Hariharan, Cheng Perng Phoo, Chia-Hsiang Kao, Hangyu Zhou, Kavita Bala, Utkarsh Mall","submitted_at":"2024-10-31T12:52:52Z","abstract_excerpt":"Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset -- $\\textit{AllClear}$ for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical imagery from Sentinel-2 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23891","kind":"arxiv","version":1},"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/2410.23891/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":"2410.23891","created_at":"2026-07-05T09:29:06.913509+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23891v1","created_at":"2026-07-05T09:29:06.913509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23891","created_at":"2026-07-05T09:29:06.913509+00:00"},{"alias_kind":"pith_short_12","alias_value":"KMMVOCSZOCAC","created_at":"2026-07-05T09:29:06.913509+00:00"},{"alias_kind":"pith_short_16","alias_value":"KMMVOCSZOCACNVKB","created_at":"2026-07-05T09:29:06.913509+00:00"},{"alias_kind":"pith_short_8","alias_value":"KMMVOCSZ","created_at":"2026-07-05T09:29:06.913509+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/KMMVOCSZOCACNVKBJTPEXJ66MC","json":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC.json","graph_json":"https://pith.science/api/pith-number/KMMVOCSZOCACNVKBJTPEXJ66MC/graph.json","events_json":"https://pith.science/api/pith-number/KMMVOCSZOCACNVKBJTPEXJ66MC/events.json","paper":"https://pith.science/paper/KMMVOCSZ"},"agent_actions":{"view_html":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC","download_json":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC.json","view_paper":"https://pith.science/paper/KMMVOCSZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23891&json=true","fetch_graph":"https://pith.science/api/pith-number/KMMVOCSZOCACNVKBJTPEXJ66MC/graph.json","fetch_events":"https://pith.science/api/pith-number/KMMVOCSZOCACNVKBJTPEXJ66MC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC/action/storage_attestation","attest_author":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC/action/author_attestation","sign_citation":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC/action/citation_signature","submit_replication":"https://pith.science/pith/KMMVOCSZOCACNVKBJTPEXJ66MC/action/replication_record"}},"created_at":"2026-07-05T09:29:06.913509+00:00","updated_at":"2026-07-05T09:29:06.913509+00:00"}