{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JLIXESUNC6U2T4XUEILGPLSP5I","short_pith_number":"pith:JLIXESUN","schema_version":"1.0","canonical_sha256":"4ad1724a8d17a9a9f2f4221667ae4fea084dcd27d25336e572fb7d98d572f957","source":{"kind":"arxiv","id":"2505.10931","version":4},"attestation_state":"computed","paper":{"title":"M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Wang, Jian Yang, Lei Luo, Wei Lu, Xiang Li","submitted_at":"2025-05-16T07:10:07Z","abstract_excerpt":"Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural details but are often affected by low-light, cloud-obscured, or low-resolution conditions, reducing the detection performance. SAR images are robust to weather, but suffer from speckle noise and limited semantic expressiveness. Optical and SAR images provide complementary advantages, and fusing them can significantly improve the detection accuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address"},"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":"2505.10931","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-16T07:10:07Z","cross_cats_sorted":[],"title_canon_sha256":"40f5829582c121afe2b1ad9a527c3e503138c02774c2f31ea9afe8bdbf3841d1","abstract_canon_sha256":"b44f8e3aae854341acf278354244a490af7ab211348a7960368db066927cdc9c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:20.350092Z","signature_b64":"y293oZ3rdYy38k8nWs5Xm7IJoMXL1amvV6twbOfufXUncg31SVepIjPGJWHwgkxBWXBDnEHyeakrWjHVxziJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ad1724a8d17a9a9f2f4221667ae4fea084dcd27d25336e572fb7d98d572f957","last_reissued_at":"2026-06-24T01:14:20.349587Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:20.349587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Wang, Jian Yang, Lei Luo, Wei Lu, Xiang Li","submitted_at":"2025-05-16T07:10:07Z","abstract_excerpt":"Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural details but are often affected by low-light, cloud-obscured, or low-resolution conditions, reducing the detection performance. SAR images are robust to weather, but suffer from speckle noise and limited semantic expressiveness. Optical and SAR images provide complementary advantages, and fusing them can significantly improve the detection accuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10931","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/2505.10931/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":"2505.10931","created_at":"2026-06-24T01:14:20.349650+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10931v4","created_at":"2026-06-24T01:14:20.349650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10931","created_at":"2026-06-24T01:14:20.349650+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLIXESUNC6U2","created_at":"2026-06-24T01:14:20.349650+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLIXESUNC6U2T4XU","created_at":"2026-06-24T01:14:20.349650+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLIXESUN","created_at":"2026-06-24T01:14:20.349650+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2605.19734","citing_title":"GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2604.08230","citing_title":"Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges","ref_index":92,"is_internal_anchor":true},{"citing_arxiv_id":"2604.14755","citing_title":"ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2605.01250","citing_title":"EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I","json":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I.json","graph_json":"https://pith.science/api/pith-number/JLIXESUNC6U2T4XUEILGPLSP5I/graph.json","events_json":"https://pith.science/api/pith-number/JLIXESUNC6U2T4XUEILGPLSP5I/events.json","paper":"https://pith.science/paper/JLIXESUN"},"agent_actions":{"view_html":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I","download_json":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I.json","view_paper":"https://pith.science/paper/JLIXESUN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10931&json=true","fetch_graph":"https://pith.science/api/pith-number/JLIXESUNC6U2T4XUEILGPLSP5I/graph.json","fetch_events":"https://pith.science/api/pith-number/JLIXESUNC6U2T4XUEILGPLSP5I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I/action/storage_attestation","attest_author":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I/action/author_attestation","sign_citation":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I/action/citation_signature","submit_replication":"https://pith.science/pith/JLIXESUNC6U2T4XUEILGPLSP5I/action/replication_record"}},"created_at":"2026-06-24T01:14:20.349650+00:00","updated_at":"2026-06-24T01:14:20.349650+00:00"}