{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IOP7JJYJWHJHR6CF4IDZFCPTBI","short_pith_number":"pith:IOP7JJYJ","schema_version":"1.0","canonical_sha256":"439ff4a709b1d278f845e2079289f30a03b5e3bdb9f820ea6b5f34a7c43b1542","source":{"kind":"arxiv","id":"2005.10745","version":2},"attestation_state":"computed","paper":{"title":"A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Carl Salvaggio, David J. Kelbe, Mohammed Yousefhussien","submitted_at":"2020-05-21T15:54:55Z","abstract_excerpt":"When 3D-point clouds from overhead sensors are used as input to remote sensing data exploitation pipelines, a large amount of effort is devoted to data preparation. Among the multiple stages of the preprocessing chain, estimating the Digital Terrain Model (DTM) model is considered to be of a high importance; however, this remains a challenge, especially for raw point clouds derived from optical imagery. Current algorithms estimate the ground points using either a set of geometrical rules that require tuning multiple parameters and human interaction, or cast the problem as a binary classificati"},"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":"2005.10745","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-21T15:54:55Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"1e840bfade03c46a9d7052024546294c5ad92f29369acd1450fc1e03dc46385c","abstract_canon_sha256":"4630ad277a4e64f14938e458ef518a2a3d07ba837acca997fe5919b2dd4ec153"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:47.443301Z","signature_b64":"VV+ne1cuoEPk+MQdgq+I9CVEh6yzQMXcjrcoRIQTV5kpt9jpnoUspaSTg4qJOEuZwpRFcBrld+JcSrPhOymGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"439ff4a709b1d278f845e2079289f30a03b5e3bdb9f820ea6b5f34a7c43b1542","last_reissued_at":"2026-07-05T01:11:47.442952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:47.442952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Carl Salvaggio, David J. Kelbe, Mohammed Yousefhussien","submitted_at":"2020-05-21T15:54:55Z","abstract_excerpt":"When 3D-point clouds from overhead sensors are used as input to remote sensing data exploitation pipelines, a large amount of effort is devoted to data preparation. Among the multiple stages of the preprocessing chain, estimating the Digital Terrain Model (DTM) model is considered to be of a high importance; however, this remains a challenge, especially for raw point clouds derived from optical imagery. Current algorithms estimate the ground points using either a set of geometrical rules that require tuning multiple parameters and human interaction, or cast the problem as a binary classificati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.10745","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/2005.10745/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":"2005.10745","created_at":"2026-07-05T01:11:47.443006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.10745v2","created_at":"2026-07-05T01:11:47.443006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.10745","created_at":"2026-07-05T01:11:47.443006+00:00"},{"alias_kind":"pith_short_12","alias_value":"IOP7JJYJWHJH","created_at":"2026-07-05T01:11:47.443006+00:00"},{"alias_kind":"pith_short_16","alias_value":"IOP7JJYJWHJHR6CF","created_at":"2026-07-05T01:11:47.443006+00:00"},{"alias_kind":"pith_short_8","alias_value":"IOP7JJYJ","created_at":"2026-07-05T01:11:47.443006+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/IOP7JJYJWHJHR6CF4IDZFCPTBI","json":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI.json","graph_json":"https://pith.science/api/pith-number/IOP7JJYJWHJHR6CF4IDZFCPTBI/graph.json","events_json":"https://pith.science/api/pith-number/IOP7JJYJWHJHR6CF4IDZFCPTBI/events.json","paper":"https://pith.science/paper/IOP7JJYJ"},"agent_actions":{"view_html":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI","download_json":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI.json","view_paper":"https://pith.science/paper/IOP7JJYJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.10745&json=true","fetch_graph":"https://pith.science/api/pith-number/IOP7JJYJWHJHR6CF4IDZFCPTBI/graph.json","fetch_events":"https://pith.science/api/pith-number/IOP7JJYJWHJHR6CF4IDZFCPTBI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI/action/storage_attestation","attest_author":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI/action/author_attestation","sign_citation":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI/action/citation_signature","submit_replication":"https://pith.science/pith/IOP7JJYJWHJHR6CF4IDZFCPTBI/action/replication_record"}},"created_at":"2026-07-05T01:11:47.443006+00:00","updated_at":"2026-07-05T01:11:47.443006+00:00"}