{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:5PV6AYLHRCNFZC5NDOJMAIBG3I","short_pith_number":"pith:5PV6AYLH","canonical_record":{"source":{"id":"2506.12456","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T11:33:40Z","cross_cats_sorted":[],"title_canon_sha256":"2fe741d974810938c619e75d97fe1a487f9fb4905d4d3bc60e94b416713b7a36","abstract_canon_sha256":"f509ac18d478e4939b9e2476c105be9dedd6b13ff10afaed9a6b88824aa58d28"},"schema_version":"1.0"},"canonical_sha256":"ebebe06167889a5c8bad1b92c02026da0428d7842d4789ce82bb60deb633a63e","source":{"kind":"arxiv","id":"2506.12456","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12456","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12456v2","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12456","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"5PV6AYLHRCNF","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"5PV6AYLHRCNFZC5N","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"5PV6AYLH","created_at":"2026-07-05T11:24:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:5PV6AYLHRCNFZC5NDOJMAIBG3I","target":"record","payload":{"canonical_record":{"source":{"id":"2506.12456","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T11:33:40Z","cross_cats_sorted":[],"title_canon_sha256":"2fe741d974810938c619e75d97fe1a487f9fb4905d4d3bc60e94b416713b7a36","abstract_canon_sha256":"f509ac18d478e4939b9e2476c105be9dedd6b13ff10afaed9a6b88824aa58d28"},"schema_version":"1.0"},"canonical_sha256":"ebebe06167889a5c8bad1b92c02026da0428d7842d4789ce82bb60deb633a63e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:23.453235Z","signature_b64":"tVtEbnCc+UIqvGx/l7TDiuIUd0niTWs0SkVtzVn7kDu3A+JtQ0GT0iH2xbxepBz4zoWR/anSK4IFYxm/6OmRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebebe06167889a5c8bad1b92c02026da0428d7842d4789ce82bb60deb633a63e","last_reissued_at":"2026-07-05T11:24:23.452525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:23.452525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.12456","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k8soK9c/6Ae3EtkmpYyh0ErJ162Ay2UU7FDk0l3b9Rfc7V7IiFsyK/708w4PwPimflIoCxqFRhOs89zgzUEDDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T13:32:34.088281Z"},"content_sha256":"cd7dbc5b5a97a25fb63b66faf44148245f85d97fe6ed8b25b23573c181a38d34","schema_version":"1.0","event_id":"sha256:cd7dbc5b5a97a25fb63b66faf44148245f85d97fe6ed8b25b23573c181a38d34"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:5PV6AYLHRCNFZC5NDOJMAIBG3I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrews Danyo, Armstrong Aboah, Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah","submitted_at":"2025-06-14T11:33:40Z","abstract_excerpt":"This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which ensures that predicted satellite imagery are consistent with demographic features, significantly enh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12456","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/2506.12456/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ed3iw35XFs06O+TyRDyR1FnIvECpBzmeUFuVlqE/5v9LcVQfAUsdauoZoL0CT3Wh8CTWLb4P3h8dk0GI+erfCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T13:32:34.089018Z"},"content_sha256":"f9d20e0143d4a8f0b820ad3207dbda341d8edb8b07761d82c8afa4c7a7610766","schema_version":"1.0","event_id":"sha256:f9d20e0143d4a8f0b820ad3207dbda341d8edb8b07761d82c8afa4c7a7610766"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/bundle.json","state_url":"https://pith.science/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T13:32:34Z","links":{"resolver":"https://pith.science/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I","bundle":"https://pith.science/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/bundle.json","state":"https://pith.science/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5PV6AYLHRCNFZC5NDOJMAIBG3I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5PV6AYLHRCNFZC5NDOJMAIBG3I","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f509ac18d478e4939b9e2476c105be9dedd6b13ff10afaed9a6b88824aa58d28","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T11:33:40Z","title_canon_sha256":"2fe741d974810938c619e75d97fe1a487f9fb4905d4d3bc60e94b416713b7a36"},"schema_version":"1.0","source":{"id":"2506.12456","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12456","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12456v2","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12456","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"5PV6AYLHRCNF","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_16","alias_value":"5PV6AYLHRCNFZC5N","created_at":"2026-07-05T11:24:23Z"},{"alias_kind":"pith_short_8","alias_value":"5PV6AYLH","created_at":"2026-07-05T11:24:23Z"}],"graph_snapshots":[{"event_id":"sha256:f9d20e0143d4a8f0b820ad3207dbda341d8edb8b07761d82c8afa4c7a7610766","target":"graph","created_at":"2026-07-05T11:24:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.12456/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which ensures that predicted satellite imagery are consistent with demographic features, significantly enh","authors_text":"Andrews Danyo, Armstrong Aboah, Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T11:33:40Z","title":"Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12456","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cd7dbc5b5a97a25fb63b66faf44148245f85d97fe6ed8b25b23573c181a38d34","target":"record","created_at":"2026-07-05T11:24:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f509ac18d478e4939b9e2476c105be9dedd6b13ff10afaed9a6b88824aa58d28","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T11:33:40Z","title_canon_sha256":"2fe741d974810938c619e75d97fe1a487f9fb4905d4d3bc60e94b416713b7a36"},"schema_version":"1.0","source":{"id":"2506.12456","kind":"arxiv","version":2}},"canonical_sha256":"ebebe06167889a5c8bad1b92c02026da0428d7842d4789ce82bb60deb633a63e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebebe06167889a5c8bad1b92c02026da0428d7842d4789ce82bb60deb633a63e","first_computed_at":"2026-07-05T11:24:23.452525Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:24:23.452525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tVtEbnCc+UIqvGx/l7TDiuIUd0niTWs0SkVtzVn7kDu3A+JtQ0GT0iH2xbxepBz4zoWR/anSK4IFYxm/6OmRDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:24:23.453235Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.12456","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd7dbc5b5a97a25fb63b66faf44148245f85d97fe6ed8b25b23573c181a38d34","sha256:f9d20e0143d4a8f0b820ad3207dbda341d8edb8b07761d82c8afa4c7a7610766"],"state_sha256":"eb08a7b809afaf8e4b6ae4f0cdbf662ddec45f0e25f687c972356acc43f9248f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A1aEJ7QR1/j0Z5/p7UqWs7bNbsdBUZdtSrLXJGal60rRV2sHziIl2UU6CCdSfWXY3jBqrIsh8EEHnCt8iSZlCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T13:32:34.095294Z","bundle_sha256":"97a1e5631a67a0b269edeed24e9c661a8fe2adc27bcf9fffd4c8b90ff47e7466"}}