{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VOQIGA5PLUGG6WSSFFT3Y6GQ6O","short_pith_number":"pith:VOQIGA5P","schema_version":"1.0","canonical_sha256":"aba08303af5d0c6f5a522967bc78d0f3b0ed9431c7044fdafbfc9efe992eb9fd","source":{"kind":"arxiv","id":"2402.19348","version":2},"attestation_state":"computed","paper":{"title":"Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Erdong Liu, Haomin Wen, Junbo Zhang, Tianrui Li, Xingchen Zou, Xixuan Hao, Yibo Yan, Yong Li, Yuehong Hu, Yuxuan Liang, Yu Zheng","submitted_at":"2024-02-29T16:56:23Z","abstract_excerpt":"As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods t"},"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":"2402.19348","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-29T16:56:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"420eff536a7ab7d93e061559d5ead3ef900b606691e941c89989324902cc7404","abstract_canon_sha256":"0aa9c22f3ad5f7db66d34d2b2cc7f78d336c589d9911fad7be52226cc3ecdb5a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:20.242491Z","signature_b64":"gG/oKU+Rv/fbRW7YaEzPpx+pr6ePAoPyCGgiI0C5DBSx+nE+CQQ0+tYvcYg2UULaYzj8GkDe3LN3CbESys5PAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aba08303af5d0c6f5a522967bc78d0f3b0ed9431c7044fdafbfc9efe992eb9fd","last_reissued_at":"2026-07-05T08:53:20.242014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:20.242014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Erdong Liu, Haomin Wen, Junbo Zhang, Tianrui Li, Xingchen Zou, Xixuan Hao, Yibo Yan, Yong Li, Yuehong Hu, Yuxuan Liang, Yu Zheng","submitted_at":"2024-02-29T16:56:23Z","abstract_excerpt":"As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.19348","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/2402.19348/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":"2402.19348","created_at":"2026-07-05T08:53:20.242071+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.19348v2","created_at":"2026-07-05T08:53:20.242071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.19348","created_at":"2026-07-05T08:53:20.242071+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOQIGA5PLUGG","created_at":"2026-07-05T08:53:20.242071+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOQIGA5PLUGG6WSS","created_at":"2026-07-05T08:53:20.242071+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOQIGA5P","created_at":"2026-07-05T08:53:20.242071+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.18464","citing_title":"MotifGPL: Motif-Enhanced Graph Prototype Learning for Deciphering Urban Social Segregation","ref_index":50,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O","json":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O.json","graph_json":"https://pith.science/api/pith-number/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/graph.json","events_json":"https://pith.science/api/pith-number/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/events.json","paper":"https://pith.science/paper/VOQIGA5P"},"agent_actions":{"view_html":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O","download_json":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O.json","view_paper":"https://pith.science/paper/VOQIGA5P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.19348&json=true","fetch_graph":"https://pith.science/api/pith-number/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/graph.json","fetch_events":"https://pith.science/api/pith-number/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/action/storage_attestation","attest_author":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/action/author_attestation","sign_citation":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/action/citation_signature","submit_replication":"https://pith.science/pith/VOQIGA5PLUGG6WSSFFT3Y6GQ6O/action/replication_record"}},"created_at":"2026-07-05T08:53:20.242071+00:00","updated_at":"2026-07-05T08:53:20.242071+00:00"}