{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NM3LCKDOD4AEBSUXRFDFCM3KKU","short_pith_number":"pith:NM3LCKDO","schema_version":"1.0","canonical_sha256":"6b36b1286e1f0040ca97894651336a553e4549ac9b1e984948588cb3a90c0698","source":{"kind":"arxiv","id":"2208.02205","version":3},"attestation_state":"computed","paper":{"title":"Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Mahdavi-Amiri, Ali Mostafavi, Cheng-Chun Lee, Navjot Kaur","submitted_at":"2022-08-03T16:41:39Z","abstract_excerpt":"This paper presents \\dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the "},"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":"2208.02205","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2022-08-03T16:41:39Z","cross_cats_sorted":[],"title_canon_sha256":"d14c808d349781c83c9da9e676aabf3e7246af493e92143d1574c0ba2ae80f23","abstract_canon_sha256":"080b96417d2dab0a05fb20bcb80275cc21bce519df119bfaddd3d2b5d6d57b4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:38:43.618161Z","signature_b64":"/h433dJZ7ojy7x53La+peczFJpWKnfcnjQqrlt0pJpxfZ3zSZl8zHSbBYOTJ/62P4H5ZFwsK3I0Td2/71nG2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b36b1286e1f0040ca97894651336a553e4549ac9b1e984948588cb3a90c0698","last_reissued_at":"2026-07-05T05:38:43.617673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:38:43.617673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ali Mahdavi-Amiri, Ali Mostafavi, Cheng-Chun Lee, Navjot Kaur","submitted_at":"2022-08-03T16:41:39Z","abstract_excerpt":"This paper presents \\dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.02205","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/2208.02205/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":"2208.02205","created_at":"2026-07-05T05:38:43.617729+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.02205v3","created_at":"2026-07-05T05:38:43.617729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.02205","created_at":"2026-07-05T05:38:43.617729+00:00"},{"alias_kind":"pith_short_12","alias_value":"NM3LCKDOD4AE","created_at":"2026-07-05T05:38:43.617729+00:00"},{"alias_kind":"pith_short_16","alias_value":"NM3LCKDOD4AEBSUX","created_at":"2026-07-05T05:38:43.617729+00:00"},{"alias_kind":"pith_short_8","alias_value":"NM3LCKDO","created_at":"2026-07-05T05:38:43.617729+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01576","citing_title":"Structured AI Decision-Making in Disaster Management","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU","json":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU.json","graph_json":"https://pith.science/api/pith-number/NM3LCKDOD4AEBSUXRFDFCM3KKU/graph.json","events_json":"https://pith.science/api/pith-number/NM3LCKDOD4AEBSUXRFDFCM3KKU/events.json","paper":"https://pith.science/paper/NM3LCKDO"},"agent_actions":{"view_html":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU","download_json":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU.json","view_paper":"https://pith.science/paper/NM3LCKDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.02205&json=true","fetch_graph":"https://pith.science/api/pith-number/NM3LCKDOD4AEBSUXRFDFCM3KKU/graph.json","fetch_events":"https://pith.science/api/pith-number/NM3LCKDOD4AEBSUXRFDFCM3KKU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU/action/storage_attestation","attest_author":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU/action/author_attestation","sign_citation":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU/action/citation_signature","submit_replication":"https://pith.science/pith/NM3LCKDOD4AEBSUXRFDFCM3KKU/action/replication_record"}},"created_at":"2026-07-05T05:38:43.617729+00:00","updated_at":"2026-07-05T05:38:43.617729+00:00"}