{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:5GOKMQ4TWPKKTKWHMQI2YZKBRP","short_pith_number":"pith:5GOKMQ4T","canonical_record":{"source":{"id":"1903.09469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-22T12:17:04Z","cross_cats_sorted":[],"title_canon_sha256":"f9bb2f186b79f7bb7c1239cf943446b3675bc5b68c256039bf550b965600ab2a","abstract_canon_sha256":"906e9a64978624ee0ae25a28caf8c12eb4d61c6858c5048605db984bfa74235f"},"schema_version":"1.0"},"canonical_sha256":"e99ca64393b3d4a9aac76411ac65418bdd1ca962402d4dbff67df8a901ee178c","source":{"kind":"arxiv","id":"1903.09469","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09469","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09469v1","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09469","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"5GOKMQ4TWPKK","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"5GOKMQ4TWPKKTKWH","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"5GOKMQ4T","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:5GOKMQ4TWPKKTKWHMQI2YZKBRP","target":"record","payload":{"canonical_record":{"source":{"id":"1903.09469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-22T12:17:04Z","cross_cats_sorted":[],"title_canon_sha256":"f9bb2f186b79f7bb7c1239cf943446b3675bc5b68c256039bf550b965600ab2a","abstract_canon_sha256":"906e9a64978624ee0ae25a28caf8c12eb4d61c6858c5048605db984bfa74235f"},"schema_version":"1.0"},"canonical_sha256":"e99ca64393b3d4a9aac76411ac65418bdd1ca962402d4dbff67df8a901ee178c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:39.554646Z","signature_b64":"MC7ZdeqQabr525Cec1/bqTyrw+YjOEfFHC3JN8dhr3VPYsmnkIkSZiNsGoP6i2T167ec8T7pcS3U8tjdj158AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e99ca64393b3d4a9aac76411ac65418bdd1ca962402d4dbff67df8a901ee178c","last_reissued_at":"2026-05-17T23:50:39.554207Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:39.554207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.09469","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":"2026-05-17T23:50:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ncuXvYf1VWunEUbdKMAZ3O+CJpGLlYDchg/wtM6m6OZ1Z/w9pe08FkKQGrUFBrkH2rGQkCRpfKYyafOKP54aBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:23:52.665357Z"},"content_sha256":"c7d176076708ff62effad749b46ec9675db87c6a1e353e48c5bea113d1e6fe93","schema_version":"1.0","event_id":"sha256:c7d176076708ff62effad749b46ec9675db87c6a1e353e48c5bea113d1e6fe93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:5GOKMQ4TWPKKTKWHMQI2YZKBRP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Aggregated Deep Local Features for Remote Sensing Image Retrieval","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Clint Sebastian, Egor Bondarev, Peter H.N. De With, Raffaele Imbriaco","submitted_at":"2019-03-22T12:17:04Z","abstract_excerpt":"Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09469","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":""},"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":"2026-05-17T23:50:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TvYEJBxiObOXta9TZKTbOu5mW6iKC46Chkx+BLqlK3Jon6+X176jX4MegDwLZ7ugss6uDl5RS8xEgcnv0UUaDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:23:52.665713Z"},"content_sha256":"651e2f6973f322c82168a471d57b5196ed122f1488d11501dfc589c9ea647ac2","schema_version":"1.0","event_id":"sha256:651e2f6973f322c82168a471d57b5196ed122f1488d11501dfc589c9ea647ac2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/bundle.json","state_url":"https://pith.science/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/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":"2026-06-05T10:23:52Z","links":{"resolver":"https://pith.science/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP","bundle":"https://pith.science/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/bundle.json","state":"https://pith.science/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5GOKMQ4TWPKKTKWHMQI2YZKBRP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:5GOKMQ4TWPKKTKWHMQI2YZKBRP","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":"906e9a64978624ee0ae25a28caf8c12eb4d61c6858c5048605db984bfa74235f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-22T12:17:04Z","title_canon_sha256":"f9bb2f186b79f7bb7c1239cf943446b3675bc5b68c256039bf550b965600ab2a"},"schema_version":"1.0","source":{"id":"1903.09469","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09469","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09469v1","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09469","created_at":"2026-05-17T23:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"5GOKMQ4TWPKK","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"5GOKMQ4TWPKKTKWH","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"5GOKMQ4T","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:651e2f6973f322c82168a471d57b5196ed122f1488d11501dfc589c9ea647ac2","target":"graph","created_at":"2026-05-17T23:50:39Z","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":"Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requ","authors_text":"Clint Sebastian, Egor Bondarev, Peter H.N. De With, Raffaele Imbriaco","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-22T12:17:04Z","title":"Aggregated Deep Local Features for Remote Sensing Image Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09469","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:c7d176076708ff62effad749b46ec9675db87c6a1e353e48c5bea113d1e6fe93","target":"record","created_at":"2026-05-17T23:50:39Z","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":"906e9a64978624ee0ae25a28caf8c12eb4d61c6858c5048605db984bfa74235f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-22T12:17:04Z","title_canon_sha256":"f9bb2f186b79f7bb7c1239cf943446b3675bc5b68c256039bf550b965600ab2a"},"schema_version":"1.0","source":{"id":"1903.09469","kind":"arxiv","version":1}},"canonical_sha256":"e99ca64393b3d4a9aac76411ac65418bdd1ca962402d4dbff67df8a901ee178c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e99ca64393b3d4a9aac76411ac65418bdd1ca962402d4dbff67df8a901ee178c","first_computed_at":"2026-05-17T23:50:39.554207Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:39.554207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MC7ZdeqQabr525Cec1/bqTyrw+YjOEfFHC3JN8dhr3VPYsmnkIkSZiNsGoP6i2T167ec8T7pcS3U8tjdj158AQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:39.554646Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.09469","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7d176076708ff62effad749b46ec9675db87c6a1e353e48c5bea113d1e6fe93","sha256:651e2f6973f322c82168a471d57b5196ed122f1488d11501dfc589c9ea647ac2"],"state_sha256":"a2088123fa2df7053ececdd11b884d54940b0c4b470390c08819e24a4e0b4fa0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lkd8z8qn6fPDXpt3FXxbQuXvFc8x5kBWIiHlQuRqkTglr2XC5PMpggpencuySwkBAr/PparQqttr9RuhyY6wDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T10:23:52.667698Z","bundle_sha256":"cccfc35212a637c60f3f0c8328349b251f25cfe5243895426bdbc02fbc0c43d7"}}