{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZKOP2BNTY3AVXTO73LIH3QMAGF","short_pith_number":"pith:ZKOP2BNT","schema_version":"1.0","canonical_sha256":"ca9cfd05b3c6c15bcddfdad07dc18031686a469d31cf704311f5ad64daceaa24","source":{"kind":"arxiv","id":"1806.03510","version":2},"attestation_state":"computed","paper":{"title":"Feature Pyramid Network for Multi-Class Land Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander V. Buslaev, Alexey A. Shvets, Selim S. Seferbekov, Vladimir I. Iglovikov","submitted_at":"2018-06-09T17:30:30Z","abstract_excerpt":"Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation "},"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":"1806.03510","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-06-09T17:30:30Z","cross_cats_sorted":[],"title_canon_sha256":"8e70b71e5792d4b39baad616c16d711773d65d54d543ac71a338d4ccf60faf7a","abstract_canon_sha256":"919d065d05417dfefc55a30ff676d8a6322eee099434fda10cc3d38ca68f049f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:47.952804Z","signature_b64":"WU25xxqoK3urNr3laJLNd/kgMShLw/L+LWLLanj3nEbK2I8sYk3dSU5A8ZBnJ0sKpfRv1aV3XWkmwvnOfPs8CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca9cfd05b3c6c15bcddfdad07dc18031686a469d31cf704311f5ad64daceaa24","last_reissued_at":"2026-05-18T00:12:47.952160Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:47.952160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature Pyramid Network for Multi-Class Land Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander V. Buslaev, Alexey A. Shvets, Selim S. Seferbekov, Vladimir I. Iglovikov","submitted_at":"2018-06-09T17:30:30Z","abstract_excerpt":"Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.03510","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1806.03510","created_at":"2026-05-18T00:12:47.952253+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.03510v2","created_at":"2026-05-18T00:12:47.952253+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.03510","created_at":"2026-05-18T00:12:47.952253+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKOP2BNTY3AV","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKOP2BNTY3AVXTO7","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKOP2BNT","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.04086","citing_title":"LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection","ref_index":81,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF","json":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF.json","graph_json":"https://pith.science/api/pith-number/ZKOP2BNTY3AVXTO73LIH3QMAGF/graph.json","events_json":"https://pith.science/api/pith-number/ZKOP2BNTY3AVXTO73LIH3QMAGF/events.json","paper":"https://pith.science/paper/ZKOP2BNT"},"agent_actions":{"view_html":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF","download_json":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF.json","view_paper":"https://pith.science/paper/ZKOP2BNT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.03510&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKOP2BNTY3AVXTO73LIH3QMAGF/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKOP2BNTY3AVXTO73LIH3QMAGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF/action/storage_attestation","attest_author":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF/action/author_attestation","sign_citation":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF/action/citation_signature","submit_replication":"https://pith.science/pith/ZKOP2BNTY3AVXTO73LIH3QMAGF/action/replication_record"}},"created_at":"2026-05-18T00:12:47.952253+00:00","updated_at":"2026-05-18T00:12:47.952253+00:00"}