{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QP7BR6BPIVRKXU5GLOAQ7LNKIB","short_pith_number":"pith:QP7BR6BP","schema_version":"1.0","canonical_sha256":"83fe18f82f4562abd3a65b810fadaa407df5401bf2fef166f61d24236d8874a9","source":{"kind":"arxiv","id":"2503.24326","version":2},"attestation_state":"computed","paper":{"title":"Self-Supervised Pretraining for Aerial Road Extraction","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"J. Marius Z\\\"ollner, Rupert Polley, Sai Vignesh Abishek Deenadayalan","submitted_at":"2025-03-31T17:14:08Z","abstract_excerpt":"Deep neural networks for aerial image segmentation require large amounts of labeled data, but high-quality aerial datasets with precise annotations are scarce and costly to produce. To address this limitation, we propose a self-supervised pretraining method that improves segmentation performance while reducing reliance on labeled data. Our approach uses inpainting-based pretraining, where the model learns to reconstruct missing regions in aerial images, capturing their inherent structure before being fine-tuned for road extraction. This method improves generalization, enhances robustness to do"},"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":"2503.24326","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-31T17:14:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"55dc01a1e5a3f866d731384602ca0127d283f2a067ea30bfb71d179dff714a0c","abstract_canon_sha256":"1e08106ec336a627b13d565fdcfb6aa8f1f12bda5c650383776d90dd0f2e8ff4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:42:46.200524Z","signature_b64":"fkvq0LuqrPw9iXpz+yTlR22oQHyrxDXv9DbTr4gmsumbxOUqGGRL7vwnMGPEketyILrPNKHxS9P87Mq2JH/bAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83fe18f82f4562abd3a65b810fadaa407df5401bf2fef166f61d24236d8874a9","last_reissued_at":"2026-07-05T10:42:46.200031Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:42:46.200031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Pretraining for Aerial Road Extraction","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"J. Marius Z\\\"ollner, Rupert Polley, Sai Vignesh Abishek Deenadayalan","submitted_at":"2025-03-31T17:14:08Z","abstract_excerpt":"Deep neural networks for aerial image segmentation require large amounts of labeled data, but high-quality aerial datasets with precise annotations are scarce and costly to produce. To address this limitation, we propose a self-supervised pretraining method that improves segmentation performance while reducing reliance on labeled data. Our approach uses inpainting-based pretraining, where the model learns to reconstruct missing regions in aerial images, capturing their inherent structure before being fine-tuned for road extraction. This method improves generalization, enhances robustness to do"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.24326","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/2503.24326/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":"2503.24326","created_at":"2026-07-05T10:42:46.200096+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.24326v2","created_at":"2026-07-05T10:42:46.200096+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.24326","created_at":"2026-07-05T10:42:46.200096+00:00"},{"alias_kind":"pith_short_12","alias_value":"QP7BR6BPIVRK","created_at":"2026-07-05T10:42:46.200096+00:00"},{"alias_kind":"pith_short_16","alias_value":"QP7BR6BPIVRKXU5G","created_at":"2026-07-05T10:42:46.200096+00:00"},{"alias_kind":"pith_short_8","alias_value":"QP7BR6BP","created_at":"2026-07-05T10:42:46.200096+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/QP7BR6BPIVRKXU5GLOAQ7LNKIB","json":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB.json","graph_json":"https://pith.science/api/pith-number/QP7BR6BPIVRKXU5GLOAQ7LNKIB/graph.json","events_json":"https://pith.science/api/pith-number/QP7BR6BPIVRKXU5GLOAQ7LNKIB/events.json","paper":"https://pith.science/paper/QP7BR6BP"},"agent_actions":{"view_html":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB","download_json":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB.json","view_paper":"https://pith.science/paper/QP7BR6BP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.24326&json=true","fetch_graph":"https://pith.science/api/pith-number/QP7BR6BPIVRKXU5GLOAQ7LNKIB/graph.json","fetch_events":"https://pith.science/api/pith-number/QP7BR6BPIVRKXU5GLOAQ7LNKIB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB/action/storage_attestation","attest_author":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB/action/author_attestation","sign_citation":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB/action/citation_signature","submit_replication":"https://pith.science/pith/QP7BR6BPIVRKXU5GLOAQ7LNKIB/action/replication_record"}},"created_at":"2026-07-05T10:42:46.200096+00:00","updated_at":"2026-07-05T10:42:46.200096+00:00"}