{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZOF7MMFVSVEBMSK24NUMBVX4RB","short_pith_number":"pith:ZOF7MMFV","schema_version":"1.0","canonical_sha256":"cb8bf630b5954816495ae368c0d6fc887436e1b5f807db8a236aeeda45737f31","source":{"kind":"arxiv","id":"2412.17573","version":1},"attestation_state":"computed","paper":{"title":"URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Song, Liang Xiao, Yawen Huang, Yefeng Zheng, Yue Sun, Ziyun Cai","submitted_at":"2024-12-23T13:45:29Z","abstract_excerpt":"The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our method, called URoadNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. URoadNet offers a novel alternative to the U-Net architecture by "},"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":"2412.17573","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-23T13:45:29Z","cross_cats_sorted":[],"title_canon_sha256":"618aebfaee58a22f59aba348085d11a0f954b192a361c0493f2eb9ae1f687c38","abstract_canon_sha256":"37fc0a3ad162540886ed49f5d61386b6cec50a3374fbb3bda4e9889e6a4b17bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:24.319408Z","signature_b64":"w/5UDCPtPOA0IUbZ77Ovj3SIbTE7gGS0YwW7UKtlEXR2QHC53H6DOKjxRQ37zk8Zywf54Dsk1KPaFbQE9CTXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb8bf630b5954816495ae368c0d6fc887436e1b5f807db8a236aeeda45737f31","last_reissued_at":"2026-07-05T09:53:24.318986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:24.318986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"URoadNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Song, Liang Xiao, Yawen Huang, Yefeng Zheng, Yue Sun, Ziyun Cai","submitted_at":"2024-12-23T13:45:29Z","abstract_excerpt":"The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our method, called URoadNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. URoadNet offers a novel alternative to the U-Net architecture by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17573","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/2412.17573/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":"2412.17573","created_at":"2026-07-05T09:53:24.319040+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17573v1","created_at":"2026-07-05T09:53:24.319040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17573","created_at":"2026-07-05T09:53:24.319040+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOF7MMFVSVEB","created_at":"2026-07-05T09:53:24.319040+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOF7MMFVSVEBMSK2","created_at":"2026-07-05T09:53:24.319040+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOF7MMFV","created_at":"2026-07-05T09:53:24.319040+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26862","citing_title":"RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB","json":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB.json","graph_json":"https://pith.science/api/pith-number/ZOF7MMFVSVEBMSK24NUMBVX4RB/graph.json","events_json":"https://pith.science/api/pith-number/ZOF7MMFVSVEBMSK24NUMBVX4RB/events.json","paper":"https://pith.science/paper/ZOF7MMFV"},"agent_actions":{"view_html":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB","download_json":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB.json","view_paper":"https://pith.science/paper/ZOF7MMFV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17573&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOF7MMFVSVEBMSK24NUMBVX4RB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOF7MMFVSVEBMSK24NUMBVX4RB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB/action/storage_attestation","attest_author":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB/action/author_attestation","sign_citation":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB/action/citation_signature","submit_replication":"https://pith.science/pith/ZOF7MMFVSVEBMSK24NUMBVX4RB/action/replication_record"}},"created_at":"2026-07-05T09:53:24.319040+00:00","updated_at":"2026-07-05T09:53:24.319040+00:00"}