{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JY3MTFN5PXRUUF3RJBDMAO76RS","short_pith_number":"pith:JY3MTFN5","schema_version":"1.0","canonical_sha256":"4e36c995bd7de34a17714846c03bfe8ca921b230dae241014b217991f2cba513","source":{"kind":"arxiv","id":"2607.11366","version":1},"attestation_state":"computed","paper":{"title":"Self-supervised training for high-resolution close-range multispectral remote sensing imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antti Lajunen, Leon-Friedrich Thomas, Mikael \\\"An\\\"akk\\\"al\\\"a","submitted_at":"2026-07-13T10:31:35Z","abstract_excerpt":"Although self-supervised learning (SSL) offers a promising way to reduce annotation effort in close-range remote sensing, its effectiveness for high-resolution multispectral unmanned aerial vehicle (UAV) imagery remains underexplored due to limited data. This study evaluated SSL pretraining for precision agriculture using cm-scale multispectral drone imagery collected across multiple sensors, years, and regions. Transformer-based encoders were pretrained with Momentum Contrast v3 (MoCo-v3) and Masked Autoencoders on a harmonized dataset combining msuav500K with newly collected multi-year UAV i"},"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":"2607.11366","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-13T10:31:35Z","cross_cats_sorted":[],"title_canon_sha256":"df0ac1d59ac7dab598b1cacff1dc146ff4ce83a26ab8ab7d9289d4276be5c3d8","abstract_canon_sha256":"b970fa15e6db2f2a22299e57654f404a708528645ced5e400a3a3192d4777a08"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:22:31.326165Z","signature_b64":"oP2QbvYGsKrcwbK9EouFuNECBE4FP4tSwF6ia4DnoEyv3cgUFdfdnUNEzp7mYGjtl1M//bsOCBowd2dZklFEAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e36c995bd7de34a17714846c03bfe8ca921b230dae241014b217991f2cba513","last_reissued_at":"2026-07-14T01:22:31.325205Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:22:31.325205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-supervised training for high-resolution close-range multispectral remote sensing imagery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antti Lajunen, Leon-Friedrich Thomas, Mikael \\\"An\\\"akk\\\"al\\\"a","submitted_at":"2026-07-13T10:31:35Z","abstract_excerpt":"Although self-supervised learning (SSL) offers a promising way to reduce annotation effort in close-range remote sensing, its effectiveness for high-resolution multispectral unmanned aerial vehicle (UAV) imagery remains underexplored due to limited data. This study evaluated SSL pretraining for precision agriculture using cm-scale multispectral drone imagery collected across multiple sensors, years, and regions. Transformer-based encoders were pretrained with Momentum Contrast v3 (MoCo-v3) and Masked Autoencoders on a harmonized dataset combining msuav500K with newly collected multi-year UAV i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11366","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/2607.11366/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":"2607.11366","created_at":"2026-07-14T01:22:31.325710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11366v1","created_at":"2026-07-14T01:22:31.325710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11366","created_at":"2026-07-14T01:22:31.325710+00:00"},{"alias_kind":"pith_short_12","alias_value":"JY3MTFN5PXRU","created_at":"2026-07-14T01:22:31.325710+00:00"},{"alias_kind":"pith_short_16","alias_value":"JY3MTFN5PXRUUF3R","created_at":"2026-07-14T01:22:31.325710+00:00"},{"alias_kind":"pith_short_8","alias_value":"JY3MTFN5","created_at":"2026-07-14T01:22:31.325710+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/JY3MTFN5PXRUUF3RJBDMAO76RS","json":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS.json","graph_json":"https://pith.science/api/pith-number/JY3MTFN5PXRUUF3RJBDMAO76RS/graph.json","events_json":"https://pith.science/api/pith-number/JY3MTFN5PXRUUF3RJBDMAO76RS/events.json","paper":"https://pith.science/paper/JY3MTFN5"},"agent_actions":{"view_html":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS","download_json":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS.json","view_paper":"https://pith.science/paper/JY3MTFN5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11366&json=true","fetch_graph":"https://pith.science/api/pith-number/JY3MTFN5PXRUUF3RJBDMAO76RS/graph.json","fetch_events":"https://pith.science/api/pith-number/JY3MTFN5PXRUUF3RJBDMAO76RS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS/action/storage_attestation","attest_author":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS/action/author_attestation","sign_citation":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS/action/citation_signature","submit_replication":"https://pith.science/pith/JY3MTFN5PXRUUF3RJBDMAO76RS/action/replication_record"}},"created_at":"2026-07-14T01:22:31.325710+00:00","updated_at":"2026-07-14T01:22:31.325710+00:00"}