{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LALUJCUJ5FM3P65ELYGENLCBRN","short_pith_number":"pith:LALUJCUJ","schema_version":"1.0","canonical_sha256":"5817448a89e959b7fba45e0c46ac418b4f3e9b84e563c49afb11808efa39d3b3","source":{"kind":"arxiv","id":"2309.16812","version":1},"attestation_state":"computed","paper":{"title":"SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hamid Askarov, Imran Ibrahimli, Ismat Bakhishov, Nabi Nabiyev, Orkhan Baghirli","submitted_at":"2023-09-28T19:39:13Z","abstract_excerpt":"Deep learning models in the Earth Observation domain heavily rely on the availability of large-scale accurately labeled satellite imagery. However, obtaining and labeling satellite imagery is a resource-intensive endeavor. While generative models offer a promising solution to address data scarcity, their potential remains underexplored. Recently, Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, divers"},"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":"2309.16812","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-09-28T19:39:13Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"c807d83d214a17a188a2146c3fe19b9eafb34f0bcf7e13420b6f75a50150fb74","abstract_canon_sha256":"fcde19031f1d96dbba5cee7f2c2b294d72024e8c9e8ceb973089404a6f758e32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:55:27.560562Z","signature_b64":"tAl6wy5ZSCAhKsQ/LEufoBrn5FsSnulRVSQYeDUr2RKdJjrXTrE22G4no0L9yg3DQzOxTITkHGd2ayXq//wrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5817448a89e959b7fba45e0c46ac418b4f3e9b84e563c49afb11808efa39d3b3","last_reissued_at":"2026-07-05T06:55:27.560146Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:55:27.560146Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hamid Askarov, Imran Ibrahimli, Ismat Bakhishov, Nabi Nabiyev, Orkhan Baghirli","submitted_at":"2023-09-28T19:39:13Z","abstract_excerpt":"Deep learning models in the Earth Observation domain heavily rely on the availability of large-scale accurately labeled satellite imagery. However, obtaining and labeling satellite imagery is a resource-intensive endeavor. While generative models offer a promising solution to address data scarcity, their potential remains underexplored. Recently, Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, divers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.16812","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/2309.16812/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":"2309.16812","created_at":"2026-07-05T06:55:27.560198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.16812v1","created_at":"2026-07-05T06:55:27.560198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.16812","created_at":"2026-07-05T06:55:27.560198+00:00"},{"alias_kind":"pith_short_12","alias_value":"LALUJCUJ5FM3","created_at":"2026-07-05T06:55:27.560198+00:00"},{"alias_kind":"pith_short_16","alias_value":"LALUJCUJ5FM3P65E","created_at":"2026-07-05T06:55:27.560198+00:00"},{"alias_kind":"pith_short_8","alias_value":"LALUJCUJ","created_at":"2026-07-05T06:55:27.560198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.04678","citing_title":"ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2602.04749","citing_title":"Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14326","citing_title":"D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14341","citing_title":"AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors","ref_index":99,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN","json":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN.json","graph_json":"https://pith.science/api/pith-number/LALUJCUJ5FM3P65ELYGENLCBRN/graph.json","events_json":"https://pith.science/api/pith-number/LALUJCUJ5FM3P65ELYGENLCBRN/events.json","paper":"https://pith.science/paper/LALUJCUJ"},"agent_actions":{"view_html":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN","download_json":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN.json","view_paper":"https://pith.science/paper/LALUJCUJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.16812&json=true","fetch_graph":"https://pith.science/api/pith-number/LALUJCUJ5FM3P65ELYGENLCBRN/graph.json","fetch_events":"https://pith.science/api/pith-number/LALUJCUJ5FM3P65ELYGENLCBRN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN/action/storage_attestation","attest_author":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN/action/author_attestation","sign_citation":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN/action/citation_signature","submit_replication":"https://pith.science/pith/LALUJCUJ5FM3P65ELYGENLCBRN/action/replication_record"}},"created_at":"2026-07-05T06:55:27.560198+00:00","updated_at":"2026-07-05T06:55:27.560198+00:00"}