{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UDGGYQPNK54P2YOOTUWIDEPEHL","short_pith_number":"pith:UDGGYQPN","schema_version":"1.0","canonical_sha256":"a0cc6c41ed5778fd61ce9d2c8191e43ac48b1daebe49610832b9089d8f953b0a","source":{"kind":"arxiv","id":"2309.02455","version":2},"attestation_state":"computed","paper":{"title":"RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmad Sebaq, Mohamed ElHelw","submitted_at":"2023-09-03T09:34:49Z","abstract_excerpt":"The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a Low-Resolution Diffusion Model (LRDM) that generates initial images based on text inputs and a Super-Resolution Diffusion Model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene co"},"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":"2309.02455","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-03T09:34:49Z","cross_cats_sorted":[],"title_canon_sha256":"f0f9fc1b9e5a37effa78803fab6653d80090a16f0e2d227c4f125679fa31dfaa","abstract_canon_sha256":"86ff9bf05c21ba36855b28b06abaa1f829e1133f6150706dc638799fa37f41b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:08.707339Z","signature_b64":"IohcS6+vqJJxYGv4NFOCuqOdC6et7OsReh7hE7+v85pcVPVUGUxt7xskL3O3AvHZLoFPUMTGU3wjBFygNZFjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0cc6c41ed5778fd61ce9d2c8191e43ac48b1daebe49610832b9089d8f953b0a","last_reissued_at":"2026-07-05T09:16:08.706803Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:08.706803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmad Sebaq, Mohamed ElHelw","submitted_at":"2023-09-03T09:34:49Z","abstract_excerpt":"The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a Low-Resolution Diffusion Model (LRDM) that generates initial images based on text inputs and a Super-Resolution Diffusion Model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02455","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/2309.02455/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":"2309.02455","created_at":"2026-07-05T09:16:08.706861+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.02455v2","created_at":"2026-07-05T09:16:08.706861+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02455","created_at":"2026-07-05T09:16:08.706861+00:00"},{"alias_kind":"pith_short_12","alias_value":"UDGGYQPNK54P","created_at":"2026-07-05T09:16:08.706861+00:00"},{"alias_kind":"pith_short_16","alias_value":"UDGGYQPNK54P2YOO","created_at":"2026-07-05T09:16:08.706861+00:00"},{"alias_kind":"pith_short_8","alias_value":"UDGGYQPN","created_at":"2026-07-05T09:16:08.706861+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.20053","citing_title":"Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL","json":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL.json","graph_json":"https://pith.science/api/pith-number/UDGGYQPNK54P2YOOTUWIDEPEHL/graph.json","events_json":"https://pith.science/api/pith-number/UDGGYQPNK54P2YOOTUWIDEPEHL/events.json","paper":"https://pith.science/paper/UDGGYQPN"},"agent_actions":{"view_html":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL","download_json":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL.json","view_paper":"https://pith.science/paper/UDGGYQPN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.02455&json=true","fetch_graph":"https://pith.science/api/pith-number/UDGGYQPNK54P2YOOTUWIDEPEHL/graph.json","fetch_events":"https://pith.science/api/pith-number/UDGGYQPNK54P2YOOTUWIDEPEHL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL/action/storage_attestation","attest_author":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL/action/author_attestation","sign_citation":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL/action/citation_signature","submit_replication":"https://pith.science/pith/UDGGYQPNK54P2YOOTUWIDEPEHL/action/replication_record"}},"created_at":"2026-07-05T09:16:08.706861+00:00","updated_at":"2026-07-05T09:16:08.706861+00:00"}