{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QOWAXJSZXGQEBXXF7FHUFRIVEA","short_pith_number":"pith:QOWAXJSZ","canonical_record":{"source":{"id":"2506.07847","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T15:09:49Z","cross_cats_sorted":[],"title_canon_sha256":"4fee98cb7c11eacf40f5ef8944bd848ef259ae21c3ae0e47bebe9e46f43d5498","abstract_canon_sha256":"9307e6a3a7b1ec61e44e17495da6ede6466c00b8761b79950a0d943d5ca9f0bd"},"schema_version":"1.0"},"canonical_sha256":"83ac0ba659b9a040dee5f94f42c5152036e780be33957f32092b35af2f0eea53","source":{"kind":"arxiv","id":"2506.07847","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07847","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07847v1","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07847","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"QOWAXJSZXGQE","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_16","alias_value":"QOWAXJSZXGQEBXXF","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_8","alias_value":"QOWAXJSZ","created_at":"2026-07-05T11:18:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QOWAXJSZXGQEBXXF7FHUFRIVEA","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07847","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T15:09:49Z","cross_cats_sorted":[],"title_canon_sha256":"4fee98cb7c11eacf40f5ef8944bd848ef259ae21c3ae0e47bebe9e46f43d5498","abstract_canon_sha256":"9307e6a3a7b1ec61e44e17495da6ede6466c00b8761b79950a0d943d5ca9f0bd"},"schema_version":"1.0"},"canonical_sha256":"83ac0ba659b9a040dee5f94f42c5152036e780be33957f32092b35af2f0eea53","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:34.351971Z","signature_b64":"Wii8Qwl3xUiGK+cFfWRhOr6qkpexCTUK1MWs00DgDEsLUz28zYp60FCBsuipNgYc0A0YEZ3E8PZcwC6ktslzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83ac0ba659b9a040dee5f94f42c5152036e780be33957f32092b35af2f0eea53","last_reissued_at":"2026-07-05T11:18:34.351566Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:34.351566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07847","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"juE766VmYLQzsdrREIZ2W38FkISp2GRirpBKSSR5j5DCXOQ6DHFEuluZbFgk7dWb/JM/l3iOSMhwUYKdeYqADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:25:03.842141Z"},"content_sha256":"507c633101156c0be751d8f1a1117dc00e89391d1436e42812224c965cddaae2","schema_version":"1.0","event_id":"sha256:507c633101156c0be751d8f1a1117dc00e89391d1436e42812224c965cddaae2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QOWAXJSZXGQEBXXF7FHUFRIVEA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hengzhi Chen, Kun Hu, Liqian Feng, Shawn Leo, Wenhua Wu, Xiaogang Zhu","submitted_at":"2025-06-09T15:09:49Z","abstract_excerpt":"Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces computational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks address this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07847","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/2506.07847/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:18:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oC5LF7+a4KmTxiLfVYsZf7mmcOj/5q+KiIA67GcCc8t/+xggV13K51ilaLrLRfrScbKkSfUm4ISSJddb/YIdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:25:03.842731Z"},"content_sha256":"f90bbe4922771d41e6ff3c1da41daf23a037b9772b28a77d2fd2baaee5fef4c8","schema_version":"1.0","event_id":"sha256:f90bbe4922771d41e6ff3c1da41daf23a037b9772b28a77d2fd2baaee5fef4c8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/bundle.json","state_url":"https://pith.science/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T17:25:03Z","links":{"resolver":"https://pith.science/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA","bundle":"https://pith.science/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/bundle.json","state":"https://pith.science/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QOWAXJSZXGQEBXXF7FHUFRIVEA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QOWAXJSZXGQEBXXF7FHUFRIVEA","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":"9307e6a3a7b1ec61e44e17495da6ede6466c00b8761b79950a0d943d5ca9f0bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T15:09:49Z","title_canon_sha256":"4fee98cb7c11eacf40f5ef8944bd848ef259ae21c3ae0e47bebe9e46f43d5498"},"schema_version":"1.0","source":{"id":"2506.07847","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07847","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07847v1","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07847","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_12","alias_value":"QOWAXJSZXGQE","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_16","alias_value":"QOWAXJSZXGQEBXXF","created_at":"2026-07-05T11:18:34Z"},{"alias_kind":"pith_short_8","alias_value":"QOWAXJSZ","created_at":"2026-07-05T11:18:34Z"}],"graph_snapshots":[{"event_id":"sha256:f90bbe4922771d41e6ff3c1da41daf23a037b9772b28a77d2fd2baaee5fef4c8","target":"graph","created_at":"2026-07-05T11:18:34Z","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/2506.07847/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces computational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks address this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized p","authors_text":"Hengzhi Chen, Kun Hu, Liqian Feng, Shawn Leo, Wenhua Wu, Xiaogang Zhu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T15:09:49Z","title":"F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07847","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":"sha256:507c633101156c0be751d8f1a1117dc00e89391d1436e42812224c965cddaae2","target":"record","created_at":"2026-07-05T11:18:34Z","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":"9307e6a3a7b1ec61e44e17495da6ede6466c00b8761b79950a0d943d5ca9f0bd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-09T15:09:49Z","title_canon_sha256":"4fee98cb7c11eacf40f5ef8944bd848ef259ae21c3ae0e47bebe9e46f43d5498"},"schema_version":"1.0","source":{"id":"2506.07847","kind":"arxiv","version":1}},"canonical_sha256":"83ac0ba659b9a040dee5f94f42c5152036e780be33957f32092b35af2f0eea53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"83ac0ba659b9a040dee5f94f42c5152036e780be33957f32092b35af2f0eea53","first_computed_at":"2026-07-05T11:18:34.351566Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:34.351566Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wii8Qwl3xUiGK+cFfWRhOr6qkpexCTUK1MWs00DgDEsLUz28zYp60FCBsuipNgYc0A0YEZ3E8PZcwC6ktslzCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:34.351971Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07847","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:507c633101156c0be751d8f1a1117dc00e89391d1436e42812224c965cddaae2","sha256:f90bbe4922771d41e6ff3c1da41daf23a037b9772b28a77d2fd2baaee5fef4c8"],"state_sha256":"e106f3ace9b107c949d889b7fdc76c6f3b9298329181fc358fbe4ec0a4cc105f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MNzvvbk1ha2klsVfHomUiEKmhEJNDLE35vPkBWKdPXNVGFoZSybRx6rBk1YVW6ls4br8s+P78EogQTdZjBzQBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:25:03.847076Z","bundle_sha256":"d278a8f2504f2704a9d0239551a1cf66de5e451bb66738d64d2103f3c6e59e1d"}}