{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HB6RFHQ76UNNPRJKE2NXB34VON","short_pith_number":"pith:HB6RFHQ7","canonical_record":{"source":{"id":"1903.12337","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T03:13:19Z","cross_cats_sorted":[],"title_canon_sha256":"0db3bc245663e266640ac831774d05d60707e02346e61c2c280e3b8f8539ad5b","abstract_canon_sha256":"2690d7a4821c51ba1fc2c65d52281c17544a4f3a2772a0398d6eb07fefaeac85"},"schema_version":"1.0"},"canonical_sha256":"387d129e1ff51ad7c52a269b70ef95734fa7d2e828393a4dc9b6d4b6f4b96aa1","source":{"kind":"arxiv","id":"1903.12337","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.12337","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"arxiv_version","alias_value":"1903.12337v2","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12337","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"pith_short_12","alias_value":"HB6RFHQ76UNN","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HB6RFHQ76UNNPRJK","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HB6RFHQ7","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HB6RFHQ76UNNPRJKE2NXB34VON","target":"record","payload":{"canonical_record":{"source":{"id":"1903.12337","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T03:13:19Z","cross_cats_sorted":[],"title_canon_sha256":"0db3bc245663e266640ac831774d05d60707e02346e61c2c280e3b8f8539ad5b","abstract_canon_sha256":"2690d7a4821c51ba1fc2c65d52281c17544a4f3a2772a0398d6eb07fefaeac85"},"schema_version":"1.0"},"canonical_sha256":"387d129e1ff51ad7c52a269b70ef95734fa7d2e828393a4dc9b6d4b6f4b96aa1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:09.038765Z","signature_b64":"yQDIbMibBiV38gRL1QUBwzLvwHztfo/Y30jMnjZxw/MmNNp9ec7TeecrMKxseCcDL3GIQ0J+WrnCAKPmyN9iCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"387d129e1ff51ad7c52a269b70ef95734fa7d2e828393a4dc9b6d4b6f4b96aa1","last_reissued_at":"2026-05-17T23:48:09.038193Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:09.038193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.12337","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"flKyRh/E+oJnqOM/iiJq2xDq9oHfMLmNBfkQ3J5XdqDC9sHcbOduT6B5+EGHFStTZxqCrlyXH60W2NwTxA+ODA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T20:26:08.583135Z"},"content_sha256":"683ccbc1e52244687d8b6686ed1ab3e5863cb2f99507847b3ec0e4ee184597f3","schema_version":"1.0","event_id":"sha256:683ccbc1e52244687d8b6686ed1ab3e5863cb2f99507847b3ec0e4ee184597f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HB6RFHQ76UNNPRJKE2NXB34VON","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangsheng Chen, Houbing Song, Jingbo Lin, Weipeng Jing","submitted_at":"2019-03-29T03:13:19Z","abstract_excerpt":"Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery. However, most of existing approaches usually require large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12337","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-17T23:48:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nmOWaraZiUWlYKcTLmqTqdMJKJq2GpmrnDrfPIccWz2lWCAuLUk2IYIjOdNmJQMDSoLjNwyEWFZMFMwpPUYuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T20:26:08.583724Z"},"content_sha256":"e29e5066f57c771804c6ed155f5962b4c53ebc5b2aebbe9fec5f6753b618fd98","schema_version":"1.0","event_id":"sha256:e29e5066f57c771804c6ed155f5962b4c53ebc5b2aebbe9fec5f6753b618fd98"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HB6RFHQ76UNNPRJKE2NXB34VON/bundle.json","state_url":"https://pith.science/pith/HB6RFHQ76UNNPRJKE2NXB34VON/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HB6RFHQ76UNNPRJKE2NXB34VON/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":"2026-05-21T20:26:08Z","links":{"resolver":"https://pith.science/pith/HB6RFHQ76UNNPRJKE2NXB34VON","bundle":"https://pith.science/pith/HB6RFHQ76UNNPRJKE2NXB34VON/bundle.json","state":"https://pith.science/pith/HB6RFHQ76UNNPRJKE2NXB34VON/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HB6RFHQ76UNNPRJKE2NXB34VON/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HB6RFHQ76UNNPRJKE2NXB34VON","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":"2690d7a4821c51ba1fc2c65d52281c17544a4f3a2772a0398d6eb07fefaeac85","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T03:13:19Z","title_canon_sha256":"0db3bc245663e266640ac831774d05d60707e02346e61c2c280e3b8f8539ad5b"},"schema_version":"1.0","source":{"id":"1903.12337","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.12337","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"arxiv_version","alias_value":"1903.12337v2","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12337","created_at":"2026-05-17T23:48:09Z"},{"alias_kind":"pith_short_12","alias_value":"HB6RFHQ76UNN","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HB6RFHQ76UNNPRJK","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HB6RFHQ7","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:e29e5066f57c771804c6ed155f5962b4c53ebc5b2aebbe9fec5f6753b618fd98","target":"graph","created_at":"2026-05-17T23:48:09Z","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"},"paper":{"abstract_excerpt":"Building footprint extraction from high-resolution aerial images is always an essential part of urban dynamic monitoring, planning and management. It has also been a challenging task in remote sensing research. In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery. However, most of existing approaches usually require large amount of parameters and floating point operations for high accuracy, it leads to high memory consumption and low inference speed which are harmful to research. In this paper, we proposed a ","authors_text":"Guangsheng Chen, Houbing Song, Jingbo Lin, Weipeng Jing","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T03:13:19Z","title":"ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12337","kind":"arxiv","version":2},"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":"sha256:683ccbc1e52244687d8b6686ed1ab3e5863cb2f99507847b3ec0e4ee184597f3","target":"record","created_at":"2026-05-17T23:48:09Z","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":"2690d7a4821c51ba1fc2c65d52281c17544a4f3a2772a0398d6eb07fefaeac85","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-29T03:13:19Z","title_canon_sha256":"0db3bc245663e266640ac831774d05d60707e02346e61c2c280e3b8f8539ad5b"},"schema_version":"1.0","source":{"id":"1903.12337","kind":"arxiv","version":2}},"canonical_sha256":"387d129e1ff51ad7c52a269b70ef95734fa7d2e828393a4dc9b6d4b6f4b96aa1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"387d129e1ff51ad7c52a269b70ef95734fa7d2e828393a4dc9b6d4b6f4b96aa1","first_computed_at":"2026-05-17T23:48:09.038193Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:09.038193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yQDIbMibBiV38gRL1QUBwzLvwHztfo/Y30jMnjZxw/MmNNp9ec7TeecrMKxseCcDL3GIQ0J+WrnCAKPmyN9iCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:09.038765Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.12337","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:683ccbc1e52244687d8b6686ed1ab3e5863cb2f99507847b3ec0e4ee184597f3","sha256:e29e5066f57c771804c6ed155f5962b4c53ebc5b2aebbe9fec5f6753b618fd98"],"state_sha256":"1526b4fb801172e77cec6b0821fde2556c287ece873c2a94a31f0e07dc66a7ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Le8HsITmwSnlQz4Z7X2wk2yUto/+1mtzdSnjzwDdtSL03+E5M3IWLVntZCh1HalguEWXuNF1T7gJOwgU4VVUBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T20:26:08.587289Z","bundle_sha256":"a1d3c77f233131e7cfa071bd37d7614ba362026728c0211e29fa75391ace3ab3"}}