{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6A6G54CHEP6ST5IFBJ4FV2SH2R","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":"b17aa69245c09ec593dffb9b02611224a7908ce8b3f9aab08ca9097d6bb0be7a","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T04:12:33Z","title_canon_sha256":"fe9ecd698e41b7811b48869351dc2499ce3009b1c50a61ad5b76c5c8df912252"},"schema_version":"1.0","source":{"id":"2606.24120","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24120","created_at":"2026-06-24T01:14:41Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24120v1","created_at":"2026-06-24T01:14:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24120","created_at":"2026-06-24T01:14:41Z"},{"alias_kind":"pith_short_12","alias_value":"6A6G54CHEP6S","created_at":"2026-06-24T01:14:41Z"},{"alias_kind":"pith_short_16","alias_value":"6A6G54CHEP6ST5IF","created_at":"2026-06-24T01:14:41Z"},{"alias_kind":"pith_short_8","alias_value":"6A6G54CH","created_at":"2026-06-24T01:14:41Z"}],"graph_snapshots":[{"event_id":"sha256:7bdb6c59759abcc5aada6b7b5699d5c4495d41beb7c76e44e6660ba40bb863a8","target":"graph","created_at":"2026-06-24T01:14:41Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.24120/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost. To produce flood maps, deep learning models for water segmentation are often used. CNN based and small vision transformer models are used. However, they need much data for adaptation to a change of scenery, i.e., another flooding event. Vision foundation models or large vision transformers are known to generalize across domains. Recently, foundation models for Earth observation ","authors_text":"Andreas Weinmann, Markus Rauhut, Ronald R\\\"osch, Thomas M\\\"arz, Tilman Bucher, Vladyslav Polushko","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T04:12:33Z","title":"Flood Mapping from RGB imagery using a Vision Foundation Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24120","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3bfc86b8cd003439637a1f2e75812a6917a9cddc06d4def5e231d63915cfc8a3","target":"record","created_at":"2026-06-24T01:14:41Z","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":"b17aa69245c09ec593dffb9b02611224a7908ce8b3f9aab08ca9097d6bb0be7a","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T04:12:33Z","title_canon_sha256":"fe9ecd698e41b7811b48869351dc2499ce3009b1c50a61ad5b76c5c8df912252"},"schema_version":"1.0","source":{"id":"2606.24120","kind":"arxiv","version":1}},"canonical_sha256":"f03c6ef04723fd29f5050a785aea47d4617fdf0110382cee55626121da37202d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f03c6ef04723fd29f5050a785aea47d4617fdf0110382cee55626121da37202d","first_computed_at":"2026-06-24T01:14:41.614856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-24T01:14:41.614856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"89VoLCyTh7Igro0bRcwHINFPr0BaOmZl+ISn9zmgrW3UZ56KpQwhWSrNTI6daHn1gAYSQy5bVrhqN7UC8FE0AQ==","signature_status":"signed_v1","signed_at":"2026-06-24T01:14:41.615294Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.24120","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bfc86b8cd003439637a1f2e75812a6917a9cddc06d4def5e231d63915cfc8a3","sha256:7bdb6c59759abcc5aada6b7b5699d5c4495d41beb7c76e44e6660ba40bb863a8"],"state_sha256":"87811ebb3a154686a5ccb9af2816ff18ee152ccaafaa6a0a43c065aad4f81952"}