{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ODEBAMEIGQXGQCKZBNKSXTF3BS","short_pith_number":"pith:ODEBAMEI","schema_version":"1.0","canonical_sha256":"70c8103088342e6809590b552bccbb0cbfcf67efb698ed72a61ab95cb31f8c4e","source":{"kind":"arxiv","id":"2605.22328","version":1},"attestation_state":"computed","paper":{"title":"3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aldino Rizaldy, Fabio Remondino, Gottfried Mandlburger, Juha Hyypp\\\"a, Markus Hollaus, Narges Takhtkeshha","submitted_at":"2026-05-21T11:18:36Z","abstract_excerpt":"Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learning (DL) enables 3D point cloud semantic segmentation; however, adoption is limited by the lack of publicly available urban and suburban MS LiDAR datasets aligned with National Mapping and Cadastral Agencies (NMCAs) classification schemes. This study addresses these gaps by introducing L1 and L2 NMCA-aligned LULC classification schemes and a new benchmark MS LiDAR dataset. We eva"},"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":"2605.22328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-21T11:18:36Z","cross_cats_sorted":[],"title_canon_sha256":"6d302dc9abb34db05d4e225927c7265cd0872ba2cf0e434ed0fe254a68677fe9","abstract_canon_sha256":"12e8f0e4fdfef2c8721c79f3bc930e3557b7a03971dc01975ba70eda32f7d0c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:38.022499Z","signature_b64":"txBgfbQOt/kkuR7v/MTewaSZN54TJzzZqkrzvvsSonRnDAqjKKhXbcpZplsVNEww8TJ24PAWgCv4ilwktiHYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70c8103088342e6809590b552bccbb0cbfcf67efb698ed72a61ab95cb31f8c4e","last_reissued_at":"2026-05-22T01:04:38.021698Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:38.021698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aldino Rizaldy, Fabio Remondino, Gottfried Mandlburger, Juha Hyypp\\\"a, Markus Hollaus, Narges Takhtkeshha","submitted_at":"2026-05-21T11:18:36Z","abstract_excerpt":"Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learning (DL) enables 3D point cloud semantic segmentation; however, adoption is limited by the lack of publicly available urban and suburban MS LiDAR datasets aligned with National Mapping and Cadastral Agencies (NMCAs) classification schemes. This study addresses these gaps by introducing L1 and L2 NMCA-aligned LULC classification schemes and a new benchmark MS LiDAR dataset. We eva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22328","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/2605.22328/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":"2605.22328","created_at":"2026-05-22T01:04:38.021833+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.22328v1","created_at":"2026-05-22T01:04:38.021833+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22328","created_at":"2026-05-22T01:04:38.021833+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODEBAMEIGQXG","created_at":"2026-05-22T01:04:38.021833+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODEBAMEIGQXGQCKZ","created_at":"2026-05-22T01:04:38.021833+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODEBAMEI","created_at":"2026-05-22T01:04:38.021833+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/ODEBAMEIGQXGQCKZBNKSXTF3BS","json":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS.json","graph_json":"https://pith.science/api/pith-number/ODEBAMEIGQXGQCKZBNKSXTF3BS/graph.json","events_json":"https://pith.science/api/pith-number/ODEBAMEIGQXGQCKZBNKSXTF3BS/events.json","paper":"https://pith.science/paper/ODEBAMEI"},"agent_actions":{"view_html":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS","download_json":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS.json","view_paper":"https://pith.science/paper/ODEBAMEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.22328&json=true","fetch_graph":"https://pith.science/api/pith-number/ODEBAMEIGQXGQCKZBNKSXTF3BS/graph.json","fetch_events":"https://pith.science/api/pith-number/ODEBAMEIGQXGQCKZBNKSXTF3BS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS/action/storage_attestation","attest_author":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS/action/author_attestation","sign_citation":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS/action/citation_signature","submit_replication":"https://pith.science/pith/ODEBAMEIGQXGQCKZBNKSXTF3BS/action/replication_record"}},"created_at":"2026-05-22T01:04:38.021833+00:00","updated_at":"2026-05-22T01:04:38.021833+00:00"}