{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:H4HKVFHBSWIBIQKSZLFDETEDXP","short_pith_number":"pith:H4HKVFHB","canonical_record":{"source":{"id":"2205.15688","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T11:08:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a92373d13eb172e8b70dba9bf44699e96b60a873d8b384d42c3d5790e51febf2","abstract_canon_sha256":"c7c888108465f96c7aff3c689999ee4774b73f83e488d5b3b1c8df0766396c7f"},"schema_version":"1.0"},"canonical_sha256":"3f0eaa94e19590144152caca324c83bbe1040c216ec8c46ce41f27f3ded3cc1c","source":{"kind":"arxiv","id":"2205.15688","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.15688","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"2205.15688v3","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15688","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"H4HKVFHBSWIB","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_16","alias_value":"H4HKVFHBSWIBIQKS","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_8","alias_value":"H4HKVFHB","created_at":"2026-07-05T04:36:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:H4HKVFHBSWIBIQKSZLFDETEDXP","target":"record","payload":{"canonical_record":{"source":{"id":"2205.15688","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T11:08:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a92373d13eb172e8b70dba9bf44699e96b60a873d8b384d42c3d5790e51febf2","abstract_canon_sha256":"c7c888108465f96c7aff3c689999ee4774b73f83e488d5b3b1c8df0766396c7f"},"schema_version":"1.0"},"canonical_sha256":"3f0eaa94e19590144152caca324c83bbe1040c216ec8c46ce41f27f3ded3cc1c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:36:00.962400Z","signature_b64":"5uA6axGJNjXAO0nKB+EmmpK0UIdztmZWbx29qZGfhSMsLYSFB5bwY6jnvhO2P1UG6XQasAYug1k2ReVfh+BRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f0eaa94e19590144152caca324c83bbe1040c216ec8c46ce41f27f3ded3cc1c","last_reissued_at":"2026-07-05T04:36:00.961912Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:36:00.961912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.15688","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F1t0X+K8W521kULheYodnWdGOGPVXW44ZB8P81skG3wkEF8OiodXYJ8dnMajvDdOQ6RHTeIacXsFaB5+zImdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:45:11.207819Z"},"content_sha256":"a10e4547bed2b34cc49aa3e35be7ee897dbc0db3f64a8ebade7e753df7928218","schema_version":"1.0","event_id":"sha256:a10e4547bed2b34cc49aa3e35be7ee897dbc0db3f64a8ebade7e753df7928218"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:H4HKVFHBSWIBIQKSZLFDETEDXP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Bruno Adriano, Jinze Yu, Yanbing Bai, Zaishuo Xia, Zelin Li","submitted_at":"2022-05-31T11:08:35Z","abstract_excerpt":"In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessment by domain experts. However, due to the lack of a large amount of labeled data, these kinds of tasks can suffer from being able to do an accurate assessment, as the efficiency of deep learning models relies highly on labeled data. Althoug"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15688","kind":"arxiv","version":3},"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/2205.15688/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":"2026-07-05T04:36:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q57LToEVCdXWrt4Z6GvJn9LQ11nBAR8uvJxVJPj63A07PfD3/c2vj+VUu1iBdjhEXbseTMkTxbitA7bNc0STCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:45:11.208308Z"},"content_sha256":"e7d2828400a5a81c66e469c673303a8bc063b48a025cca71a9f723629d008938","schema_version":"1.0","event_id":"sha256:e7d2828400a5a81c66e469c673303a8bc063b48a025cca71a9f723629d008938"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/bundle.json","state_url":"https://pith.science/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/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":"2026-08-03T19:45:11Z","links":{"resolver":"https://pith.science/pith/H4HKVFHBSWIBIQKSZLFDETEDXP","bundle":"https://pith.science/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/bundle.json","state":"https://pith.science/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H4HKVFHBSWIBIQKSZLFDETEDXP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:H4HKVFHBSWIBIQKSZLFDETEDXP","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":"c7c888108465f96c7aff3c689999ee4774b73f83e488d5b3b1c8df0766396c7f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T11:08:35Z","title_canon_sha256":"a92373d13eb172e8b70dba9bf44699e96b60a873d8b384d42c3d5790e51febf2"},"schema_version":"1.0","source":{"id":"2205.15688","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.15688","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"arxiv_version","alias_value":"2205.15688v3","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15688","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_12","alias_value":"H4HKVFHBSWIB","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_16","alias_value":"H4HKVFHBSWIBIQKS","created_at":"2026-07-05T04:36:00Z"},{"alias_kind":"pith_short_8","alias_value":"H4HKVFHB","created_at":"2026-07-05T04:36:00Z"}],"graph_snapshots":[{"event_id":"sha256:e7d2828400a5a81c66e469c673303a8bc063b48a025cca71a9f723629d008938","target":"graph","created_at":"2026-07-05T04:36:00Z","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/2205.15688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessment by domain experts. However, due to the lack of a large amount of labeled data, these kinds of tasks can suffer from being able to do an accurate assessment, as the efficiency of deep learning models relies highly on labeled data. Althoug","authors_text":"Bruno Adriano, Jinze Yu, Yanbing Bai, Zaishuo Xia, Zelin Li","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T11:08:35Z","title":"Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15688","kind":"arxiv","version":3},"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:a10e4547bed2b34cc49aa3e35be7ee897dbc0db3f64a8ebade7e753df7928218","target":"record","created_at":"2026-07-05T04:36:00Z","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":"c7c888108465f96c7aff3c689999ee4774b73f83e488d5b3b1c8df0766396c7f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T11:08:35Z","title_canon_sha256":"a92373d13eb172e8b70dba9bf44699e96b60a873d8b384d42c3d5790e51febf2"},"schema_version":"1.0","source":{"id":"2205.15688","kind":"arxiv","version":3}},"canonical_sha256":"3f0eaa94e19590144152caca324c83bbe1040c216ec8c46ce41f27f3ded3cc1c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f0eaa94e19590144152caca324c83bbe1040c216ec8c46ce41f27f3ded3cc1c","first_computed_at":"2026-07-05T04:36:00.961912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:36:00.961912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5uA6axGJNjXAO0nKB+EmmpK0UIdztmZWbx29qZGfhSMsLYSFB5bwY6jnvhO2P1UG6XQasAYug1k2ReVfh+BRDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:36:00.962400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.15688","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a10e4547bed2b34cc49aa3e35be7ee897dbc0db3f64a8ebade7e753df7928218","sha256:e7d2828400a5a81c66e469c673303a8bc063b48a025cca71a9f723629d008938"],"state_sha256":"58720bada28b12c44f46d7adfad5bdc4bb6b9ccda27390343b9b7c286f1c2206"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ew0p8mdoqFOJnZFS3AnhKxYZE7VjAgCcI/Yw3UlAfqKrcy9urF8IdUQA6aU7VNsUeVFxYDEd7RH76yu7OcTeDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:45:11.211568Z","bundle_sha256":"014278a972a5849beffca0369b748070d4b3fe5a606fa2367e75594c07224460"}}