{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:HI7QEC5C7G4FZU5X6KE2QPOCTZ","short_pith_number":"pith:HI7QEC5C","schema_version":"1.0","canonical_sha256":"3a3f020ba2f9b85cd3b7f289a83dc29e4535644da400bd7d6ec884e5990ed7d6","source":{"kind":"arxiv","id":"1609.06846","version":1},"attestation_state":"computed","paper":{"title":"Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bertrand Le Saux (Palaiseau), Nicolas Audebert (OBELIX, Palaiseau), S\\'ebastien Lef\\`evre (OBELIX)","submitted_at":"2016-09-22T07:42:06Z","abstract_excerpt":"This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous s"},"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":"1609.06846","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-22T07:42:06Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"9700ac796aee3b82b4a7fe6775347ad5beeae6a7eaefc42b99ed56936acb8b27","abstract_canon_sha256":"369c5fe2a6ea1072ec68cdbecf7fdcb4547cbcf66060e96225bfcefd7358ad07"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:04.972182Z","signature_b64":"cJANc+/NI2so7C7MuesallqPg2VVEIWRNkVa/30vdueYQS9JzWon4WRqA++qoJ+otcxg5//j+ayMgOzi8fgiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a3f020ba2f9b85cd3b7f289a83dc29e4535644da400bd7d6ec884e5990ed7d6","last_reissued_at":"2026-05-18T01:04:04.971623Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:04.971623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bertrand Le Saux (Palaiseau), Nicolas Audebert (OBELIX, Palaiseau), S\\'ebastien Lef\\`evre (OBELIX)","submitted_at":"2016-09-22T07:42:06Z","abstract_excerpt":"This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.06846","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1609.06846","created_at":"2026-05-18T01:04:04.971715+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.06846v1","created_at":"2026-05-18T01:04:04.971715+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.06846","created_at":"2026-05-18T01:04:04.971715+00:00"},{"alias_kind":"pith_short_12","alias_value":"HI7QEC5C7G4F","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"HI7QEC5C7G4FZU5X","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"HI7QEC5C","created_at":"2026-05-18T12:30:19.053100+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/HI7QEC5C7G4FZU5X6KE2QPOCTZ","json":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ.json","graph_json":"https://pith.science/api/pith-number/HI7QEC5C7G4FZU5X6KE2QPOCTZ/graph.json","events_json":"https://pith.science/api/pith-number/HI7QEC5C7G4FZU5X6KE2QPOCTZ/events.json","paper":"https://pith.science/paper/HI7QEC5C"},"agent_actions":{"view_html":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ","download_json":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ.json","view_paper":"https://pith.science/paper/HI7QEC5C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.06846&json=true","fetch_graph":"https://pith.science/api/pith-number/HI7QEC5C7G4FZU5X6KE2QPOCTZ/graph.json","fetch_events":"https://pith.science/api/pith-number/HI7QEC5C7G4FZU5X6KE2QPOCTZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ/action/storage_attestation","attest_author":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ/action/author_attestation","sign_citation":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ/action/citation_signature","submit_replication":"https://pith.science/pith/HI7QEC5C7G4FZU5X6KE2QPOCTZ/action/replication_record"}},"created_at":"2026-05-18T01:04:04.971715+00:00","updated_at":"2026-05-18T01:04:04.971715+00:00"}