{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HECSWRNZNRHKAAQOIQTTMVK6M3","short_pith_number":"pith:HECSWRNZ","schema_version":"1.0","canonical_sha256":"39052b45b96c4ea0020e442736555e66d423a5c01857ac9a9032a8360e2e9db8","source":{"kind":"arxiv","id":"2410.23278","version":1},"attestation_state":"computed","paper":{"title":"OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gaofeng Meng, Haochen Wang, Hongbo Zhao, Lue Fan, Xiaojuan Jin, Yixin Zhang, Yuntao Chen, Yuran Yang, Zhaoxiang Zhang","submitted_at":"2024-10-30T17:56:02Z","abstract_excerpt":"In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transportation industry, such as navigation and autonomous driving. Extracting road structures from satellite images is an efficient way to construct large-scale maps. However, existing satellite datasets provide only coarse semantic-level labels with a relatively low resolution (up to level 19), impeding the advancement of this field. In contrast, the proposed OpenSatMap (1) has fine-grained instance-level annotations; (2) "},"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":"2410.23278","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-30T17:56:02Z","cross_cats_sorted":[],"title_canon_sha256":"c8f9201c8a19a6562efe03f7d77b1321bc91d7aeb88d50cf9ef002c760beafa0","abstract_canon_sha256":"fd3a5b18bbf0ff97b6d5da260f417f156556ed818da9112eefea9a1492107083"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:43.196576Z","signature_b64":"crpWXQ469KMFfWHkWl/UTMbG9nK/eE+ClptQB0Krifz+EvUVsgcnZWMTmeleNwiwv1DCdQJFY3C3IK5LMlzQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39052b45b96c4ea0020e442736555e66d423a5c01857ac9a9032a8360e2e9db8","last_reissued_at":"2026-07-05T09:28:43.196084Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:43.196084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gaofeng Meng, Haochen Wang, Hongbo Zhao, Lue Fan, Xiaojuan Jin, Yixin Zhang, Yuntao Chen, Yuran Yang, Zhaoxiang Zhang","submitted_at":"2024-10-30T17:56:02Z","abstract_excerpt":"In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transportation industry, such as navigation and autonomous driving. Extracting road structures from satellite images is an efficient way to construct large-scale maps. However, existing satellite datasets provide only coarse semantic-level labels with a relatively low resolution (up to level 19), impeding the advancement of this field. In contrast, the proposed OpenSatMap (1) has fine-grained instance-level annotations; (2) "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23278","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/2410.23278/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":"2410.23278","created_at":"2026-07-05T09:28:43.196143+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.23278v1","created_at":"2026-07-05T09:28:43.196143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23278","created_at":"2026-07-05T09:28:43.196143+00:00"},{"alias_kind":"pith_short_12","alias_value":"HECSWRNZNRHK","created_at":"2026-07-05T09:28:43.196143+00:00"},{"alias_kind":"pith_short_16","alias_value":"HECSWRNZNRHKAAQO","created_at":"2026-07-05T09:28:43.196143+00:00"},{"alias_kind":"pith_short_8","alias_value":"HECSWRNZ","created_at":"2026-07-05T09:28:43.196143+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3","json":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3.json","graph_json":"https://pith.science/api/pith-number/HECSWRNZNRHKAAQOIQTTMVK6M3/graph.json","events_json":"https://pith.science/api/pith-number/HECSWRNZNRHKAAQOIQTTMVK6M3/events.json","paper":"https://pith.science/paper/HECSWRNZ"},"agent_actions":{"view_html":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3","download_json":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3.json","view_paper":"https://pith.science/paper/HECSWRNZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.23278&json=true","fetch_graph":"https://pith.science/api/pith-number/HECSWRNZNRHKAAQOIQTTMVK6M3/graph.json","fetch_events":"https://pith.science/api/pith-number/HECSWRNZNRHKAAQOIQTTMVK6M3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3/action/storage_attestation","attest_author":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3/action/author_attestation","sign_citation":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3/action/citation_signature","submit_replication":"https://pith.science/pith/HECSWRNZNRHKAAQOIQTTMVK6M3/action/replication_record"}},"created_at":"2026-07-05T09:28:43.196143+00:00","updated_at":"2026-07-05T09:28:43.196143+00:00"}