{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:OIULPUFGCWZU3M7JCELUPWYCMW","short_pith_number":"pith:OIULPUFG","schema_version":"1.0","canonical_sha256":"7228b7d0a615b34db3e9111747db0265b46b7666d3d6bd0429ed67ed08795236","source":{"kind":"arxiv","id":"2110.01967","version":2},"attestation_state":"computed","paper":{"title":"Season-invariant GNSS-denied visual localization for UAVs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Francesco Verdoja, Jouko Kinnari, Ville Kyrki","submitted_at":"2021-10-05T11:55:00Z","abstract_excerpt":"Localization without Global Navigation Satellite Systems (GNSS) is a critical functionality in autonomous operations of unmanned aerial vehicles (UAVs). Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season, and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained convolutional neural network model that is"},"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":"2110.01967","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-10-05T11:55:00Z","cross_cats_sorted":[],"title_canon_sha256":"9bbab0088563f812b52c2797053a1c14d3b9e94f5146f8e42f7f46cc1aa1096b","abstract_canon_sha256":"3e027c7f2902c76a3f0e7f5d4324224e103250a97c1e74b12af65240a42f8123"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:15.014374Z","signature_b64":"iKBBgBG8cRexCXqHr2kGHyhjdzAeXLcUEqXThf1CEUoNA9+t8rS7lL0SUl9ZCNmJ5axE5Q6fH3W4YbrZZ75aDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7228b7d0a615b34db3e9111747db0265b46b7666d3d6bd0429ed67ed08795236","last_reissued_at":"2026-07-05T09:58:15.013859Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:15.013859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Season-invariant GNSS-denied visual localization for UAVs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Francesco Verdoja, Jouko Kinnari, Ville Kyrki","submitted_at":"2021-10-05T11:55:00Z","abstract_excerpt":"Localization without Global Navigation Satellite Systems (GNSS) is a critical functionality in autonomous operations of unmanned aerial vehicles (UAVs). Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season, and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained convolutional neural network model that is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.01967","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/2110.01967/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":"2110.01967","created_at":"2026-07-05T09:58:15.013912+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.01967v2","created_at":"2026-07-05T09:58:15.013912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.01967","created_at":"2026-07-05T09:58:15.013912+00:00"},{"alias_kind":"pith_short_12","alias_value":"OIULPUFGCWZU","created_at":"2026-07-05T09:58:15.013912+00:00"},{"alias_kind":"pith_short_16","alias_value":"OIULPUFGCWZU3M7J","created_at":"2026-07-05T09:58:15.013912+00:00"},{"alias_kind":"pith_short_8","alias_value":"OIULPUFG","created_at":"2026-07-05T09:58:15.013912+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/OIULPUFGCWZU3M7JCELUPWYCMW","json":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW.json","graph_json":"https://pith.science/api/pith-number/OIULPUFGCWZU3M7JCELUPWYCMW/graph.json","events_json":"https://pith.science/api/pith-number/OIULPUFGCWZU3M7JCELUPWYCMW/events.json","paper":"https://pith.science/paper/OIULPUFG"},"agent_actions":{"view_html":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW","download_json":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW.json","view_paper":"https://pith.science/paper/OIULPUFG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.01967&json=true","fetch_graph":"https://pith.science/api/pith-number/OIULPUFGCWZU3M7JCELUPWYCMW/graph.json","fetch_events":"https://pith.science/api/pith-number/OIULPUFGCWZU3M7JCELUPWYCMW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW/action/storage_attestation","attest_author":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW/action/author_attestation","sign_citation":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW/action/citation_signature","submit_replication":"https://pith.science/pith/OIULPUFGCWZU3M7JCELUPWYCMW/action/replication_record"}},"created_at":"2026-07-05T09:58:15.013912+00:00","updated_at":"2026-07-05T09:58:15.013912+00:00"}