{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:LY5CQA6F4DZ7VAWTVDPO2NKFYC","short_pith_number":"pith:LY5CQA6F","schema_version":"1.0","canonical_sha256":"5e3a2803c5e0f3fa82d3a8deed3545c0bd1463eaafa55a2cf2d44d8a7d2ae3be","source":{"kind":"arxiv","id":"2009.08188","version":1},"attestation_state":"computed","paper":{"title":"Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC","eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexandre X. Falc\\~ao, Devis Tuia, John E. Vargas-Mu\\~noz","submitted_at":"2020-09-17T10:05:30Z","abstract_excerpt":"Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automat"},"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":"2009.08188","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2020-09-17T10:05:30Z","cross_cats_sorted":["cs.HC","eess.IV"],"title_canon_sha256":"926de4cc94030e024e2f6d23a9dc530471ae11a7e0ddf926c08422fd22fb1532","abstract_canon_sha256":"62404692264c9769fa135ae4cd568d85048e17a3ab1d68f146d216fe6372de28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:36:11.324688Z","signature_b64":"uyMEBS61aQBmF1hB5ijmS/Fkd2RyYl1BEsSpEMfil7ISMq+CQ2PpA1ZpcJb9Nncuq1rQ6J8pDkaE+axnw6MVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e3a2803c5e0f3fa82d3a8deed3545c0bd1463eaafa55a2cf2d44d8a7d2ae3be","last_reissued_at":"2026-07-05T01:36:11.324340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:36:11.324340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC","eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexandre X. Falc\\~ao, Devis Tuia, John E. Vargas-Mu\\~noz","submitted_at":"2020-09-17T10:05:30Z","abstract_excerpt":"Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.08188","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/2009.08188/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":"2009.08188","created_at":"2026-07-05T01:36:11.324404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.08188v1","created_at":"2026-07-05T01:36:11.324404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.08188","created_at":"2026-07-05T01:36:11.324404+00:00"},{"alias_kind":"pith_short_12","alias_value":"LY5CQA6F4DZ7","created_at":"2026-07-05T01:36:11.324404+00:00"},{"alias_kind":"pith_short_16","alias_value":"LY5CQA6F4DZ7VAWT","created_at":"2026-07-05T01:36:11.324404+00:00"},{"alias_kind":"pith_short_8","alias_value":"LY5CQA6F","created_at":"2026-07-05T01:36:11.324404+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/LY5CQA6F4DZ7VAWTVDPO2NKFYC","json":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC.json","graph_json":"https://pith.science/api/pith-number/LY5CQA6F4DZ7VAWTVDPO2NKFYC/graph.json","events_json":"https://pith.science/api/pith-number/LY5CQA6F4DZ7VAWTVDPO2NKFYC/events.json","paper":"https://pith.science/paper/LY5CQA6F"},"agent_actions":{"view_html":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC","download_json":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC.json","view_paper":"https://pith.science/paper/LY5CQA6F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.08188&json=true","fetch_graph":"https://pith.science/api/pith-number/LY5CQA6F4DZ7VAWTVDPO2NKFYC/graph.json","fetch_events":"https://pith.science/api/pith-number/LY5CQA6F4DZ7VAWTVDPO2NKFYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC/action/storage_attestation","attest_author":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC/action/author_attestation","sign_citation":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC/action/citation_signature","submit_replication":"https://pith.science/pith/LY5CQA6F4DZ7VAWTVDPO2NKFYC/action/replication_record"}},"created_at":"2026-07-05T01:36:11.324404+00:00","updated_at":"2026-07-05T01:36:11.324404+00:00"}