{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:P42LCR2BZPV4PELMYJCDSEZIFB","short_pith_number":"pith:P42LCR2B","schema_version":"1.0","canonical_sha256":"7f34b14741cbebc7916cc244391328286cebc725b8e5f5a8ce4bbdce00eb6cc1","source":{"kind":"arxiv","id":"2208.09411","version":2},"attestation_state":"computed","paper":{"title":"Wildfire Forecasting with Satellite Images and Deep Generative Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Schmidt, Sang Truong, Thai-Nam Hoang","submitted_at":"2022-08-19T15:52:43Z","abstract_excerpt":"Wildfire forecasting has been one of the most critical tasks that humanities want to thrive. It plays a vital role in protecting human life. Wildfire prediction, on the other hand, is difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images as a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts is based on stochastic image-autoregressive recurrent networks, wh"},"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":"2208.09411","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-08-19T15:52:43Z","cross_cats_sorted":[],"title_canon_sha256":"5ed36f5c8efeca877d84d48539ff0e812f024b411e21507ee215631ac181d7e9","abstract_canon_sha256":"061aa18ae451a0de48b7c595e26fb5405827c80e24fcfdf431bbbfabe410ff0f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:02.609716Z","signature_b64":"CIWHvAJ+BS+sKXHio9fx5/+bTmDhgRO6M7xvBeWq5WwVgzJmY35DHScrHQLvJfQ2/SZuqzGg15v/BJqdVlfaCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f34b14741cbebc7916cc244391328286cebc725b8e5f5a8ce4bbdce00eb6cc1","last_reissued_at":"2026-07-05T05:21:02.609278Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:02.609278Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wildfire Forecasting with Satellite Images and Deep Generative Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chris Schmidt, Sang Truong, Thai-Nam Hoang","submitted_at":"2022-08-19T15:52:43Z","abstract_excerpt":"Wildfire forecasting has been one of the most critical tasks that humanities want to thrive. It plays a vital role in protecting human life. Wildfire prediction, on the other hand, is difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images as a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts is based on stochastic image-autoregressive recurrent networks, wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09411","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/2208.09411/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":"2208.09411","created_at":"2026-07-05T05:21:02.609358+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09411v2","created_at":"2026-07-05T05:21:02.609358+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09411","created_at":"2026-07-05T05:21:02.609358+00:00"},{"alias_kind":"pith_short_12","alias_value":"P42LCR2BZPV4","created_at":"2026-07-05T05:21:02.609358+00:00"},{"alias_kind":"pith_short_16","alias_value":"P42LCR2BZPV4PELM","created_at":"2026-07-05T05:21:02.609358+00:00"},{"alias_kind":"pith_short_8","alias_value":"P42LCR2B","created_at":"2026-07-05T05:21:02.609358+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.08690","citing_title":"CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB","json":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB.json","graph_json":"https://pith.science/api/pith-number/P42LCR2BZPV4PELMYJCDSEZIFB/graph.json","events_json":"https://pith.science/api/pith-number/P42LCR2BZPV4PELMYJCDSEZIFB/events.json","paper":"https://pith.science/paper/P42LCR2B"},"agent_actions":{"view_html":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB","download_json":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB.json","view_paper":"https://pith.science/paper/P42LCR2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09411&json=true","fetch_graph":"https://pith.science/api/pith-number/P42LCR2BZPV4PELMYJCDSEZIFB/graph.json","fetch_events":"https://pith.science/api/pith-number/P42LCR2BZPV4PELMYJCDSEZIFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB/action/storage_attestation","attest_author":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB/action/author_attestation","sign_citation":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB/action/citation_signature","submit_replication":"https://pith.science/pith/P42LCR2BZPV4PELMYJCDSEZIFB/action/replication_record"}},"created_at":"2026-07-05T05:21:02.609358+00:00","updated_at":"2026-07-05T05:21:02.609358+00:00"}