{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:D77NE5J3QC5GTRVOIQ4AAELU4I","short_pith_number":"pith:D77NE5J3","schema_version":"1.0","canonical_sha256":"1ffed2753b80ba69c6ae4438001174e23fc0850df87009aa56bac49681da7597","source":{"kind":"arxiv","id":"2302.05109","version":2},"attestation_state":"computed","paper":{"title":"Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangyang Lei, Jianwei Fan, Liang Zhou, Mengmeng Wang, Yao Qin, Yuanxin Ye","submitted_at":"2023-02-10T08:21:01Z","abstract_excerpt":"Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in this article. First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images. Then, to alleviate 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":"2302.05109","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-02-10T08:21:01Z","cross_cats_sorted":[],"title_canon_sha256":"c08c585db36601a9a4ada21e5d513ed2d842be1cee0817d8feab7362ccaa6f8e","abstract_canon_sha256":"39bde9e8a2a874ca5712d3aa9fbe0eb228cc5c319a93f558cf6fdd5a9135a28d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:14.852184Z","signature_b64":"CX/gmJyVA5Hxmes419zSo5i/S31ZvJqyAiGp+wx1fuh3dWfr8miLtx+THSHUkkAp8Yh7+TVBH20zfFsyK5TvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ffed2753b80ba69c6ae4438001174e23fc0850df87009aa56bac49681da7597","last_reissued_at":"2026-07-05T07:34:14.851661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:14.851661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangyang Lei, Jianwei Fan, Liang Zhou, Mengmeng Wang, Yao Qin, Yuanxin Ye","submitted_at":"2023-02-10T08:21:01Z","abstract_excerpt":"Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in this article. First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images. Then, to alleviate the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.05109","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.05109/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":"2302.05109","created_at":"2026-07-05T07:34:14.851723+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.05109v2","created_at":"2026-07-05T07:34:14.851723+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.05109","created_at":"2026-07-05T07:34:14.851723+00:00"},{"alias_kind":"pith_short_12","alias_value":"D77NE5J3QC5G","created_at":"2026-07-05T07:34:14.851723+00:00"},{"alias_kind":"pith_short_16","alias_value":"D77NE5J3QC5GTRVO","created_at":"2026-07-05T07:34:14.851723+00:00"},{"alias_kind":"pith_short_8","alias_value":"D77NE5J3","created_at":"2026-07-05T07:34:14.851723+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I","json":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I.json","graph_json":"https://pith.science/api/pith-number/D77NE5J3QC5GTRVOIQ4AAELU4I/graph.json","events_json":"https://pith.science/api/pith-number/D77NE5J3QC5GTRVOIQ4AAELU4I/events.json","paper":"https://pith.science/paper/D77NE5J3"},"agent_actions":{"view_html":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I","download_json":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I.json","view_paper":"https://pith.science/paper/D77NE5J3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.05109&json=true","fetch_graph":"https://pith.science/api/pith-number/D77NE5J3QC5GTRVOIQ4AAELU4I/graph.json","fetch_events":"https://pith.science/api/pith-number/D77NE5J3QC5GTRVOIQ4AAELU4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I/action/storage_attestation","attest_author":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I/action/author_attestation","sign_citation":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I/action/citation_signature","submit_replication":"https://pith.science/pith/D77NE5J3QC5GTRVOIQ4AAELU4I/action/replication_record"}},"created_at":"2026-07-05T07:34:14.851723+00:00","updated_at":"2026-07-05T07:34:14.851723+00:00"}