{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ONYJKSEYTVISCN4IILEDWJA2I6","short_pith_number":"pith:ONYJKSEY","schema_version":"1.0","canonical_sha256":"73709548989d5121378842c83b241a47b3adaae6f018d46814a3186a23808e82","source":{"kind":"arxiv","id":"2405.04634","version":4},"attestation_state":"computed","paper":{"title":"FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Charles Gaydon, Floryne Roche, Michel Daab","submitted_at":"2024-05-07T19:37:22Z","abstract_excerpt":"Mapping agencies are increasingly adopting Aerial Lidar Scanning (ALS) as a new tool to map buildings and other above-ground structures. Processing ALS data at scale requires efficient point classification methods that perform well over highly diverse territories. Large annotated Lidar datasets are needed to evaluate these classification methods, however, current Lidar benchmarks have restricted scope and often cover a single urban area. To bridge this data gap, we introduce the FRench ALS Clouds from TArgeted Landscapes (FRACTAL) dataset: an ultra-large-scale aerial Lidar dataset made of 100,"},"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":"2405.04634","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-07T19:37:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0724ee8e36b8843afc5f85b49769368691ec843d0e2e9ffaae4b5d6096cd8cf7","abstract_canon_sha256":"92026a02393d122fa91066798cf089b02a8d7e41a818fd7dc96e3f29bb267720"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:01:34.030304Z","signature_b64":"VG/UQgMeQ9/SumuOJExGPfMNPoFjxuUTMGU9/44gujVVYHQvZX8vMutquIxy/l2hMsrwQe+u321UNYelqs94Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73709548989d5121378842c83b241a47b3adaae6f018d46814a3186a23808e82","last_reissued_at":"2026-07-05T09:01:34.029871Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:01:34.029871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Charles Gaydon, Floryne Roche, Michel Daab","submitted_at":"2024-05-07T19:37:22Z","abstract_excerpt":"Mapping agencies are increasingly adopting Aerial Lidar Scanning (ALS) as a new tool to map buildings and other above-ground structures. Processing ALS data at scale requires efficient point classification methods that perform well over highly diverse territories. Large annotated Lidar datasets are needed to evaluate these classification methods, however, current Lidar benchmarks have restricted scope and often cover a single urban area. To bridge this data gap, we introduce the FRench ALS Clouds from TArgeted Landscapes (FRACTAL) dataset: an ultra-large-scale aerial Lidar dataset made of 100,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.04634","kind":"arxiv","version":4},"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/2405.04634/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":"2405.04634","created_at":"2026-07-05T09:01:34.029923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.04634v4","created_at":"2026-07-05T09:01:34.029923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.04634","created_at":"2026-07-05T09:01:34.029923+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONYJKSEYTVIS","created_at":"2026-07-05T09:01:34.029923+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONYJKSEYTVISCN4I","created_at":"2026-07-05T09:01:34.029923+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONYJKSEY","created_at":"2026-07-05T09:01:34.029923+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03784","citing_title":"Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6","json":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6.json","graph_json":"https://pith.science/api/pith-number/ONYJKSEYTVISCN4IILEDWJA2I6/graph.json","events_json":"https://pith.science/api/pith-number/ONYJKSEYTVISCN4IILEDWJA2I6/events.json","paper":"https://pith.science/paper/ONYJKSEY"},"agent_actions":{"view_html":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6","download_json":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6.json","view_paper":"https://pith.science/paper/ONYJKSEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.04634&json=true","fetch_graph":"https://pith.science/api/pith-number/ONYJKSEYTVISCN4IILEDWJA2I6/graph.json","fetch_events":"https://pith.science/api/pith-number/ONYJKSEYTVISCN4IILEDWJA2I6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6/action/storage_attestation","attest_author":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6/action/author_attestation","sign_citation":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6/action/citation_signature","submit_replication":"https://pith.science/pith/ONYJKSEYTVISCN4IILEDWJA2I6/action/replication_record"}},"created_at":"2026-07-05T09:01:34.029923+00:00","updated_at":"2026-07-05T09:01:34.029923+00:00"}