{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:5TAWTY26VUYUZ42PWF77K4B6OG","short_pith_number":"pith:5TAWTY26","schema_version":"1.0","canonical_sha256":"ecc169e35ead314cf34fb17ff5703e71bb8d27d8dd95ac72f060c37d326b6dc1","source":{"kind":"arxiv","id":"2007.09547","version":1},"attestation_state":"computed","paper":{"title":"Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amin Sadeghi, Favyen Bastani, Hari Balakrishnan, Mohamed M. Elshrif, Mohammad Alizadeh, Samuel Madden, Sanjay Chawla, Satvat Jagwani, Songtao He","submitted_at":"2020-07-19T01:04:19Z","abstract_excerpt":"Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations.\n  In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, "},"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":"2007.09547","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-19T01:04:19Z","cross_cats_sorted":[],"title_canon_sha256":"4647bc1cde6e134369740a4de5d5c9f8dd69a2df17a8c4d332fe78463e2285a8","abstract_canon_sha256":"886e7f2dd61d8d12ea651098ec01bb260847627f0f19458ffad6117d502aaee5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:20:26.133538Z","signature_b64":"GIM+5MZ4+fGDLuk9qnJLRmwSqPZAntbDe1ciedL6EYr4Yx6/XM76QKzKE9Zv+IrjgQu1q10ptZBsnPpLWWjTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecc169e35ead314cf34fb17ff5703e71bb8d27d8dd95ac72f060c37d326b6dc1","last_reissued_at":"2026-07-05T01:20:26.133051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:20:26.133051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amin Sadeghi, Favyen Bastani, Hari Balakrishnan, Mohamed M. Elshrif, Mohammad Alizadeh, Samuel Madden, Sanjay Chawla, Satvat Jagwani, Songtao He","submitted_at":"2020-07-19T01:04:19Z","abstract_excerpt":"Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations.\n  In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.09547","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/2007.09547/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":"2007.09547","created_at":"2026-07-05T01:20:26.133103+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.09547v1","created_at":"2026-07-05T01:20:26.133103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.09547","created_at":"2026-07-05T01:20:26.133103+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TAWTY26VUYU","created_at":"2026-07-05T01:20:26.133103+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TAWTY26VUYUZ42P","created_at":"2026-07-05T01:20:26.133103+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TAWTY26","created_at":"2026-07-05T01:20:26.133103+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.17534","citing_title":"Learning Isometric Embeddings of Road Networks using Multidimensional Scaling","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG","json":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG.json","graph_json":"https://pith.science/api/pith-number/5TAWTY26VUYUZ42PWF77K4B6OG/graph.json","events_json":"https://pith.science/api/pith-number/5TAWTY26VUYUZ42PWF77K4B6OG/events.json","paper":"https://pith.science/paper/5TAWTY26"},"agent_actions":{"view_html":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG","download_json":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG.json","view_paper":"https://pith.science/paper/5TAWTY26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.09547&json=true","fetch_graph":"https://pith.science/api/pith-number/5TAWTY26VUYUZ42PWF77K4B6OG/graph.json","fetch_events":"https://pith.science/api/pith-number/5TAWTY26VUYUZ42PWF77K4B6OG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG/action/storage_attestation","attest_author":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG/action/author_attestation","sign_citation":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG/action/citation_signature","submit_replication":"https://pith.science/pith/5TAWTY26VUYUZ42PWF77K4B6OG/action/replication_record"}},"created_at":"2026-07-05T01:20:26.133103+00:00","updated_at":"2026-07-05T01:20:26.133103+00:00"}