{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:GW242WNWK4EME2N7S2NTBKONLJ","short_pith_number":"pith:GW242WNW","canonical_record":{"source":{"id":"1608.00775","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-02T11:33:44Z","cross_cats_sorted":[],"title_canon_sha256":"08cf83ceff8e4be581cfed2c8e00d7724cd6d9bd7e3afe12bb8c308d6f958a73","abstract_canon_sha256":"f73b68a0ea4193ee356685f29096ca87d0feb90711dd177a38fad3e0f36b678d"},"schema_version":"1.0"},"canonical_sha256":"35b5cd59b65708c269bf969b30a9cd5a6a8540a59642c8c9bac299ac94149ac1","source":{"kind":"arxiv","id":"1608.00775","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00775","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00775v2","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00775","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"pith_short_12","alias_value":"GW242WNWK4EM","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GW242WNWK4EME2N7","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GW242WNW","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:GW242WNWK4EME2N7S2NTBKONLJ","target":"record","payload":{"canonical_record":{"source":{"id":"1608.00775","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-02T11:33:44Z","cross_cats_sorted":[],"title_canon_sha256":"08cf83ceff8e4be581cfed2c8e00d7724cd6d9bd7e3afe12bb8c308d6f958a73","abstract_canon_sha256":"f73b68a0ea4193ee356685f29096ca87d0feb90711dd177a38fad3e0f36b678d"},"schema_version":"1.0"},"canonical_sha256":"35b5cd59b65708c269bf969b30a9cd5a6a8540a59642c8c9bac299ac94149ac1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:16.346059Z","signature_b64":"31uwcweh5R1j9nl67ijo2UNsuJHw6kAg9FS7hnWHuNIWxNdkstFImL63IsrNIKZg/KSN573gmwWb7roK6GzlBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35b5cd59b65708c269bf969b30a9cd5a6a8540a59642c8c9bac299ac94149ac1","last_reissued_at":"2026-05-18T00:49:16.345385Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:16.345385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.00775","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V+15FSw9l1whKHo3qNc9byO25FnlyPkwDqY7IxxmGfFHfl9uroJBJ0vd/oR07ann35nftSlj6/dQul2vOH06Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T18:40:10.605442Z"},"content_sha256":"65efaeabfcce11eec064e299cb6d312887fccbe0b6a3be85d2399a6a8026dde0","schema_version":"1.0","event_id":"sha256:65efaeabfcce11eec064e299cb6d312887fccbe0b6a3be85d2399a6a8026dde0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:GW242WNWK4EME2N7S2NTBKONLJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Devis Tuia, Michele Volpi","submitted_at":"2016-08-02T11:33:44Z","abstract_excerpt":"Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction.\n  In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them bac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00775","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tyYq71N3DBnycCCy17geRZExryVUHPGozzsJkFaA15PlGhizggxtmiv5uNso9GPLaTY4Gz0/mbzxyk3x5VxVCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T18:40:10.605948Z"},"content_sha256":"7c7934fd6dd7d5c7cd343bd9409f3a3ff1b93665fd0c8f2e761a4595a2bec996","schema_version":"1.0","event_id":"sha256:7c7934fd6dd7d5c7cd343bd9409f3a3ff1b93665fd0c8f2e761a4595a2bec996"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GW242WNWK4EME2N7S2NTBKONLJ/bundle.json","state_url":"https://pith.science/pith/GW242WNWK4EME2N7S2NTBKONLJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GW242WNWK4EME2N7S2NTBKONLJ/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":"2026-05-27T18:40:10Z","links":{"resolver":"https://pith.science/pith/GW242WNWK4EME2N7S2NTBKONLJ","bundle":"https://pith.science/pith/GW242WNWK4EME2N7S2NTBKONLJ/bundle.json","state":"https://pith.science/pith/GW242WNWK4EME2N7S2NTBKONLJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GW242WNWK4EME2N7S2NTBKONLJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:GW242WNWK4EME2N7S2NTBKONLJ","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":"f73b68a0ea4193ee356685f29096ca87d0feb90711dd177a38fad3e0f36b678d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-02T11:33:44Z","title_canon_sha256":"08cf83ceff8e4be581cfed2c8e00d7724cd6d9bd7e3afe12bb8c308d6f958a73"},"schema_version":"1.0","source":{"id":"1608.00775","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00775","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00775v2","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00775","created_at":"2026-05-18T00:49:16Z"},{"alias_kind":"pith_short_12","alias_value":"GW242WNWK4EM","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GW242WNWK4EME2N7","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GW242WNW","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:7c7934fd6dd7d5c7cd343bd9409f3a3ff1b93665fd0c8f2e761a4595a2bec996","target":"graph","created_at":"2026-05-18T00:49:16Z","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":"Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction.\n  In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them bac","authors_text":"Devis Tuia, Michele Volpi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-02T11:33:44Z","title":"Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00775","kind":"arxiv","version":2},"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":"sha256:65efaeabfcce11eec064e299cb6d312887fccbe0b6a3be85d2399a6a8026dde0","target":"record","created_at":"2026-05-18T00:49:16Z","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":"f73b68a0ea4193ee356685f29096ca87d0feb90711dd177a38fad3e0f36b678d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-02T11:33:44Z","title_canon_sha256":"08cf83ceff8e4be581cfed2c8e00d7724cd6d9bd7e3afe12bb8c308d6f958a73"},"schema_version":"1.0","source":{"id":"1608.00775","kind":"arxiv","version":2}},"canonical_sha256":"35b5cd59b65708c269bf969b30a9cd5a6a8540a59642c8c9bac299ac94149ac1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35b5cd59b65708c269bf969b30a9cd5a6a8540a59642c8c9bac299ac94149ac1","first_computed_at":"2026-05-18T00:49:16.345385Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:16.345385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"31uwcweh5R1j9nl67ijo2UNsuJHw6kAg9FS7hnWHuNIWxNdkstFImL63IsrNIKZg/KSN573gmwWb7roK6GzlBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:16.346059Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.00775","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65efaeabfcce11eec064e299cb6d312887fccbe0b6a3be85d2399a6a8026dde0","sha256:7c7934fd6dd7d5c7cd343bd9409f3a3ff1b93665fd0c8f2e761a4595a2bec996"],"state_sha256":"034fdb532ba762ddac91a628a651ab0032cbcc88c32a4d026884330d764ee6de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qM6gNJ3TnfvzcUvjGPz/eaAtzDa1kH+eG5FE2TAV/lpKM6nweYj9GLTVSh2336V/DvAF1tbieCzc5Y3EjaXzCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T18:40:10.609090Z","bundle_sha256":"73603a45e7308053c88edee9cb423027269a85431c757eda6427ea2afc432c93"}}