{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XPRHBVKTK5DEG5M3PCTSW3UADI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"34b4c2d6bcfc31ea1897621ee74d598fb1d07f4492f107f3fd86e09a630806ba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T23:30:22Z","title_canon_sha256":"7df233b146854478518d8da0b73861b2b35bb3246dc47228620b5f7d98842a9a"},"schema_version":"1.0","source":{"id":"2604.23066","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.23066","created_at":"2026-06-09T01:05:17Z"},{"alias_kind":"arxiv_version","alias_value":"2604.23066v2","created_at":"2026-06-09T01:05:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.23066","created_at":"2026-06-09T01:05:17Z"},{"alias_kind":"pith_short_12","alias_value":"XPRHBVKTK5DE","created_at":"2026-06-09T01:05:17Z"},{"alias_kind":"pith_short_16","alias_value":"XPRHBVKTK5DEG5M3","created_at":"2026-06-09T01:05:17Z"},{"alias_kind":"pith_short_8","alias_value":"XPRHBVKT","created_at":"2026-06-09T01:05:17Z"}],"graph_snapshots":[{"event_id":"sha256:2992748b55c50c3fc991b1117b7e91e45485b77ffb73fe8bc8a39f7d852cb35f","target":"graph","created_at":"2026-06-09T01:05:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. ... achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products ... which yielded IoUs of 44.1 and 48.1, respectively."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The hand-labeling by experts accurately captures all visible surface water without significant errors from shadows, vegetation, or urban features, and the 14 selected events and chips provide sufficient diversity and representativeness for training generalizable models."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"UFO is a new publicly available hand-labeled dataset of 215 PlanetScope image chips from 14 urban flood events annotated for inundated and non-inundated areas, validated via segmentation model with 77.3 mean IoU and comparisons to existing water products."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A hand-labeled dataset of 215 satellite image chips enables machine learning models to map urban flood inundation at 77.3 mean IoU."}],"snapshot_sha256":"e5ff290c8369db58f711c632c13a9eb9f039d28f029b772d34fa523554a46da7"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T09:40:12.366662Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T23:32:19.480936Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.23066/integrity.json","findings":[],"snapshot_sha256":"61c3c2d03fa743457e79792cc260202440698d7d07cd8751107f688429fc0079","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-ex","authors_text":"Ariful Islam, Beth Tellman, Hannah K. Friedrich, Jonathan Giezendanner, Rohit Mukherjee, Upmanu Lall, Venkataraman Lakshmi, Zhijie Zhang","cross_cats":[],"headline":"A hand-labeled dataset of 215 satellite image chips enables machine learning models to map urban flood inundation at 77.3 mean IoU.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T23:30:22Z","title":"Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.23066","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-08T12:11:17.900269Z","id":"e5274c38-8dca-48d7-9df4-b0daea570756","model_set":{"reader":"grok-4.3"},"one_line_summary":"UFO is a new publicly available hand-labeled dataset of 215 PlanetScope image chips from 14 urban flood events annotated for inundated and non-inundated areas, validated via segmentation model with 77.3 mean IoU and comparisons to existing water products.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A hand-labeled dataset of 215 satellite image chips enables machine learning models to map urban flood inundation at 77.3 mean IoU.","strongest_claim":"We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. ... achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products ... which yielded IoUs of 44.1 and 48.1, respectively.","weakest_assumption":"The hand-labeling by experts accurately captures all visible surface water without significant errors from shadows, vegetation, or urban features, and the 14 selected events and chips provide sufficient diversity and representativeness for training generalizable models."}},"verdict_id":"e5274c38-8dca-48d7-9df4-b0daea570756"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:be7be54c6eaf71c98c7706a17821f825407612ca91162bf0938bd60c28b87251","target":"record","created_at":"2026-06-09T01:05:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"34b4c2d6bcfc31ea1897621ee74d598fb1d07f4492f107f3fd86e09a630806ba","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-04-24T23:30:22Z","title_canon_sha256":"7df233b146854478518d8da0b73861b2b35bb3246dc47228620b5f7d98842a9a"},"schema_version":"1.0","source":{"id":"2604.23066","kind":"arxiv","version":2}},"canonical_sha256":"bbe270d553574643759b78a72b6e801a29648a26656717b16f624c34638bade5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bbe270d553574643759b78a72b6e801a29648a26656717b16f624c34638bade5","first_computed_at":"2026-06-09T01:05:17.904229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:17.904229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gSlOAlpEkg7Q7VfyJQRzY1MXFfr99kDa7ru+yM3h6H3XhMXr8kI/tG/L9998n6EDmfN2PRL9IcHJhSS+jhyWDg==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:17.904664Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.23066","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be7be54c6eaf71c98c7706a17821f825407612ca91162bf0938bd60c28b87251","sha256:2992748b55c50c3fc991b1117b7e91e45485b77ffb73fe8bc8a39f7d852cb35f"],"state_sha256":"3feed695d428342eeeaf8aae2871376dbfea334f18a091fa4d9b097dd8bdad38"}