{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5XQLOKOOWX6GMAB7JTSHK3ATZ6","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":"586d69ab4db93af2bcb2359c4736ed674bf900448689b046d5f49b0c2fe83f40","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-07T23:53:20Z","title_canon_sha256":"6150ba8ea6944d69bfdf2ca437608d6b3e56a1bf4af74cf1a3cac586aa4effa5"},"schema_version":"1.0","source":{"id":"2202.04755","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.04755","created_at":"2026-07-05T03:55:53Z"},{"alias_kind":"arxiv_version","alias_value":"2202.04755v1","created_at":"2026-07-05T03:55:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.04755","created_at":"2026-07-05T03:55:53Z"},{"alias_kind":"pith_short_12","alias_value":"5XQLOKOOWX6G","created_at":"2026-07-05T03:55:53Z"},{"alias_kind":"pith_short_16","alias_value":"5XQLOKOOWX6GMAB7","created_at":"2026-07-05T03:55:53Z"},{"alias_kind":"pith_short_8","alias_value":"5XQLOKOO","created_at":"2026-07-05T03:55:53Z"}],"graph_snapshots":[{"event_id":"sha256:769c236cf0b9a3618170047f2269a508700836cf6f3e2fb7c026845f7c621de2","target":"graph","created_at":"2026-07-05T03:55:53Z","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/2202.04755/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research, we propose a deep convolutional neural network, namely Deep Spatial Scene Network (DeepSSN), to better assess the spatial scene similarity. In DeepSSN, a triplet loss function is designed as a comprehensive distance metric to support the similarity ","authors_text":"Danhuai Guo, Ran Tao, Shiyin Ge, Shu Zhang, Song Gao, Yangang Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-07T23:53:20Z","title":"DeepSSN: a deep convolutional neural network to assess spatial scene similarity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.04755","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:d1152975df055a2f45020ba114be63fa614ab065fd66ff5ac45fd50b7b2408c8","target":"record","created_at":"2026-07-05T03:55:53Z","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":"586d69ab4db93af2bcb2359c4736ed674bf900448689b046d5f49b0c2fe83f40","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-07T23:53:20Z","title_canon_sha256":"6150ba8ea6944d69bfdf2ca437608d6b3e56a1bf4af74cf1a3cac586aa4effa5"},"schema_version":"1.0","source":{"id":"2202.04755","kind":"arxiv","version":1}},"canonical_sha256":"ede0b729ceb5fc66003f4ce4756c13cfa84e3a5b70409917b1a58cfabfcc6b66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ede0b729ceb5fc66003f4ce4756c13cfa84e3a5b70409917b1a58cfabfcc6b66","first_computed_at":"2026-07-05T03:55:53.629713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:55:53.629713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zPX6lJBHGuvh8LKrSWdO6kHE/Ro15Il0IdiRodaXivMqB1Fo2oja30XRAxDk712Rdo5Fkvejynq3OW6F8WZdAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:55:53.630122Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.04755","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1152975df055a2f45020ba114be63fa614ab065fd66ff5ac45fd50b7b2408c8","sha256:769c236cf0b9a3618170047f2269a508700836cf6f3e2fb7c026845f7c621de2"],"state_sha256":"450473117189892b989245a0f38dd5da9a029831bc620ee23209b81bb85ee8ed"}