{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KGX4DL7VWMWBUCSTQCC2ZHISJH","short_pith_number":"pith:KGX4DL7V","schema_version":"1.0","canonical_sha256":"51afc1aff5b32c1a0a538085ac9d1249ed26d64fe701ee918eb1a2a0debc2ddd","source":{"kind":"arxiv","id":"2606.10174","version":1},"attestation_state":"computed","paper":{"title":"A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam J. Watts, Ahmet Enis Cetin, B. Ugur Toreyin, Emadeldeen Hamdan, Erdem Koyuncu, Ugur Gudukbay, Yingyi Luo","submitted_at":"2026-06-08T21:07:53Z","abstract_excerpt":"Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we introduce GWFP (Global Wildfire Prevention Dataset), a large-scale, open-source dataset of wildfire images and videos designed to support early fire and smoke detection research. GWFP contains geographically diverse wildfire scenes, including flames, smoke, Waterdog/Fog environmental conditions, Near Infrared (NIR) imagery, Ember, and challenging negative samples collected from real-world scenarios worldwide. To evaluate dataset robustness and cross-"},"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":"2606.10174","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-08T21:07:53Z","cross_cats_sorted":[],"title_canon_sha256":"13c8904159d9de4f30719f47bf86526ce0bf64f90c83d76005c764053204691f","abstract_canon_sha256":"f8d35a09655ca1d5b950b674d98188716101b78ef284bf09339faee55c880a22"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:08:58.138631Z","signature_b64":"dNRenQ0GnKSzUfxAAj5ofbM0nnTi7meVs7BjQBcJjX3+zMvMg1H2p38OGPYrD4/NHY6IEwWi7wIYKO6pGM7lAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51afc1aff5b32c1a0a538085ac9d1249ed26d64fe701ee918eb1a2a0debc2ddd","last_reissued_at":"2026-06-10T01:08:58.137728Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:08:58.137728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam J. Watts, Ahmet Enis Cetin, B. Ugur Toreyin, Emadeldeen Hamdan, Erdem Koyuncu, Ugur Gudukbay, Yingyi Luo","submitted_at":"2026-06-08T21:07:53Z","abstract_excerpt":"Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we introduce GWFP (Global Wildfire Prevention Dataset), a large-scale, open-source dataset of wildfire images and videos designed to support early fire and smoke detection research. GWFP contains geographically diverse wildfire scenes, including flames, smoke, Waterdog/Fog environmental conditions, Near Infrared (NIR) imagery, Ember, and challenging negative samples collected from real-world scenarios worldwide. To evaluate dataset robustness and cross-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10174","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/2606.10174/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":"2606.10174","created_at":"2026-06-10T01:08:58.137869+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10174v1","created_at":"2026-06-10T01:08:58.137869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10174","created_at":"2026-06-10T01:08:58.137869+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGX4DL7VWMWB","created_at":"2026-06-10T01:08:58.137869+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGX4DL7VWMWBUCST","created_at":"2026-06-10T01:08:58.137869+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGX4DL7V","created_at":"2026-06-10T01:08:58.137869+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/KGX4DL7VWMWBUCSTQCC2ZHISJH","json":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH.json","graph_json":"https://pith.science/api/pith-number/KGX4DL7VWMWBUCSTQCC2ZHISJH/graph.json","events_json":"https://pith.science/api/pith-number/KGX4DL7VWMWBUCSTQCC2ZHISJH/events.json","paper":"https://pith.science/paper/KGX4DL7V"},"agent_actions":{"view_html":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH","download_json":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH.json","view_paper":"https://pith.science/paper/KGX4DL7V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10174&json=true","fetch_graph":"https://pith.science/api/pith-number/KGX4DL7VWMWBUCSTQCC2ZHISJH/graph.json","fetch_events":"https://pith.science/api/pith-number/KGX4DL7VWMWBUCSTQCC2ZHISJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH/action/storage_attestation","attest_author":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH/action/author_attestation","sign_citation":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH/action/citation_signature","submit_replication":"https://pith.science/pith/KGX4DL7VWMWBUCSTQCC2ZHISJH/action/replication_record"}},"created_at":"2026-06-10T01:08:58.137869+00:00","updated_at":"2026-06-10T01:08:58.137869+00:00"}