{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3Q2BJZWFM3BV6NNLTCVIELZ62U","short_pith_number":"pith:3Q2BJZWF","schema_version":"1.0","canonical_sha256":"dc3414e6c566c35f35ab98aa822f3ed5190f8b5a69f9bb3103fe2074a713dcd6","source":{"kind":"arxiv","id":"1707.03383","version":1},"attestation_state":"computed","paper":{"title":"A step towards procedural terrain generation with GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Christopher Beckham, Christopher Pal","submitted_at":"2017-07-11T17:44:20Z","abstract_excerpt":"Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA."},"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":"1707.03383","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T17:44:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"317a970d76ac45985b6aa63bdce7c429f467d6b4ea2066d6437d3dac2516e34c","abstract_canon_sha256":"6c58b7fc33102d03287322012bffe1dd975f640d9b053abf1406077d3b51740c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:29.196559Z","signature_b64":"KRp8EY1yI3FprUQ14d2+5VjMSOmcGCqLmQwKo/S1guPinrc1DDMJ1OQh/+NB0mZYnBq6Ibb0l4qJ1XnaCAlfDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc3414e6c566c35f35ab98aa822f3ed5190f8b5a69f9bb3103fe2074a713dcd6","last_reissued_at":"2026-05-18T00:40:29.196091Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:29.196091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A step towards procedural terrain generation with GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"stat.ML","authors_text":"Christopher Beckham, Christopher Pal","submitted_at":"2017-07-11T17:44:20Z","abstract_excerpt":"Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03383","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":""},"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":"1707.03383","created_at":"2026-05-18T00:40:29.196154+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.03383v1","created_at":"2026-05-18T00:40:29.196154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03383","created_at":"2026-05-18T00:40:29.196154+00:00"},{"alias_kind":"pith_short_12","alias_value":"3Q2BJZWFM3BV","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3Q2BJZWFM3BV6NNL","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3Q2BJZWF","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.08309","citing_title":"InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U","json":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U.json","graph_json":"https://pith.science/api/pith-number/3Q2BJZWFM3BV6NNLTCVIELZ62U/graph.json","events_json":"https://pith.science/api/pith-number/3Q2BJZWFM3BV6NNLTCVIELZ62U/events.json","paper":"https://pith.science/paper/3Q2BJZWF"},"agent_actions":{"view_html":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U","download_json":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U.json","view_paper":"https://pith.science/paper/3Q2BJZWF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.03383&json=true","fetch_graph":"https://pith.science/api/pith-number/3Q2BJZWFM3BV6NNLTCVIELZ62U/graph.json","fetch_events":"https://pith.science/api/pith-number/3Q2BJZWFM3BV6NNLTCVIELZ62U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U/action/storage_attestation","attest_author":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U/action/author_attestation","sign_citation":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U/action/citation_signature","submit_replication":"https://pith.science/pith/3Q2BJZWFM3BV6NNLTCVIELZ62U/action/replication_record"}},"created_at":"2026-05-18T00:40:29.196154+00:00","updated_at":"2026-05-18T00:40:29.196154+00:00"}