{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4UNGJH5AQURKVNF55VFCOAX6SJ","short_pith_number":"pith:4UNGJH5A","schema_version":"1.0","canonical_sha256":"e51a649fa08522aab4bded4a2702fe927a8731dfeaf93091a0607abcfbecf8a2","source":{"kind":"arxiv","id":"2406.10225","version":2},"attestation_state":"computed","paper":{"title":"SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Song, Liyue Shen, Zhaoxu Luo","submitted_at":"2024-06-14T17:58:28Z","abstract_excerpt":"During the acquisition of satellite images, there is generally a trade-off between spatial resolution and temporal resolution (acquisition frequency) due to the onboard sensors of satellite imaging systems. High-resolution satellite images are very important for land crop monitoring, urban planning, wildfire management and a variety of applications. It is a significant yet challenging task to achieve high spatial-temporal resolution in satellite imaging. With the advent of diffusion models, we can now learn strong generative priors to generate realistic satellite images with high resolution, w"},"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":"2406.10225","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T17:58:28Z","cross_cats_sorted":[],"title_canon_sha256":"4a82dec76f1eb88927616fce17371d3fb902ee4d6dcfe5937eab12918deb1951","abstract_canon_sha256":"d1eee904f739914dea665910685e2a398f35b06a1fd80aa874947883303c7d8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:08.785073Z","signature_b64":"4fFN2g8Sv18u/hocYDngZTXOLU+TmDjelJrEqAbcIdoulAGePGbs1wljt03TAeMfRwU+/e/Uoz4sAsj68/4XBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e51a649fa08522aab4bded4a2702fe927a8731dfeaf93091a0607abcfbecf8a2","last_reissued_at":"2026-07-05T09:37:08.784514Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:08.784514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Song, Liyue Shen, Zhaoxu Luo","submitted_at":"2024-06-14T17:58:28Z","abstract_excerpt":"During the acquisition of satellite images, there is generally a trade-off between spatial resolution and temporal resolution (acquisition frequency) due to the onboard sensors of satellite imaging systems. High-resolution satellite images are very important for land crop monitoring, urban planning, wildfire management and a variety of applications. It is a significant yet challenging task to achieve high spatial-temporal resolution in satellite imaging. With the advent of diffusion models, we can now learn strong generative priors to generate realistic satellite images with high resolution, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10225","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/2406.10225/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":"2406.10225","created_at":"2026-07-05T09:37:08.784573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.10225v2","created_at":"2026-07-05T09:37:08.784573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10225","created_at":"2026-07-05T09:37:08.784573+00:00"},{"alias_kind":"pith_short_12","alias_value":"4UNGJH5AQURK","created_at":"2026-07-05T09:37:08.784573+00:00"},{"alias_kind":"pith_short_16","alias_value":"4UNGJH5AQURKVNF5","created_at":"2026-07-05T09:37:08.784573+00:00"},{"alias_kind":"pith_short_8","alias_value":"4UNGJH5A","created_at":"2026-07-05T09:37:08.784573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.11524","citing_title":"Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using Airborne LiDAR HD Reference Data across Metropolitan France","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ","json":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ.json","graph_json":"https://pith.science/api/pith-number/4UNGJH5AQURKVNF55VFCOAX6SJ/graph.json","events_json":"https://pith.science/api/pith-number/4UNGJH5AQURKVNF55VFCOAX6SJ/events.json","paper":"https://pith.science/paper/4UNGJH5A"},"agent_actions":{"view_html":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ","download_json":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ.json","view_paper":"https://pith.science/paper/4UNGJH5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.10225&json=true","fetch_graph":"https://pith.science/api/pith-number/4UNGJH5AQURKVNF55VFCOAX6SJ/graph.json","fetch_events":"https://pith.science/api/pith-number/4UNGJH5AQURKVNF55VFCOAX6SJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ/action/storage_attestation","attest_author":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ/action/author_attestation","sign_citation":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ/action/citation_signature","submit_replication":"https://pith.science/pith/4UNGJH5AQURKVNF55VFCOAX6SJ/action/replication_record"}},"created_at":"2026-07-05T09:37:08.784573+00:00","updated_at":"2026-07-05T09:37:08.784573+00:00"}