{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TPWABYNEGN6RXGQZNR3ICRF2XY","short_pith_number":"pith:TPWABYNE","schema_version":"1.0","canonical_sha256":"9bec00e1a4337d1b9a196c768144babe01d40519b2b2a9bb05fcf529dbc9c40d","source":{"kind":"arxiv","id":"1905.11893","version":2},"attestation_state":"computed","paper":{"title":"BreizhCrops: A Time Series Dataset for Crop Type Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Charlotte Pelletier, Marco K\\\"orner, Marc Ru{\\ss}wurm, Maximilian Zollner, S\\'ebastien Lef\\`evre","submitted_at":"2019-05-28T15:40:18Z","abstract_excerpt":"We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/BreizhCrops) that has been designed with applicability fo"},"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":"1905.11893","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-28T15:40:18Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"5ad99f5397a0f1d430e111197cbc91b3a8dba01bb3fe3e6bc9f4cf0bc82c4664","abstract_canon_sha256":"cb60260c358750e2bb77f2f28683ca6da899ed83f6d9fd7cd962ea0c76fbff86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:01:35.016904Z","signature_b64":"l3EfRFgbBC3rQEiQoWKPCqcSVXnGuCGZcQypBoHgm0pzNLMVnzvVKQTvk5OIlwmz8ivwhkzgF04DkRhfQIBLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bec00e1a4337d1b9a196c768144babe01d40519b2b2a9bb05fcf529dbc9c40d","last_reissued_at":"2026-07-05T01:01:35.016500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:01:35.016500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BreizhCrops: A Time Series Dataset for Crop Type Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Charlotte Pelletier, Marco K\\\"orner, Marc Ru{\\ss}wurm, Maximilian Zollner, S\\'ebastien Lef\\`evre","submitted_at":"2019-05-28T15:40:18Z","abstract_excerpt":"We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven recently proposed deep neural networks along with a Random Forest baseline. The dataset, model (re-)implementations and pre-trained model weights are available at the associated GitHub repository (https://github.com/dl4sits/BreizhCrops) that has been designed with applicability fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11893","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/1905.11893/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":"1905.11893","created_at":"2026-07-05T01:01:35.016558+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.11893v2","created_at":"2026-07-05T01:01:35.016558+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11893","created_at":"2026-07-05T01:01:35.016558+00:00"},{"alias_kind":"pith_short_12","alias_value":"TPWABYNEGN6R","created_at":"2026-07-05T01:01:35.016558+00:00"},{"alias_kind":"pith_short_16","alias_value":"TPWABYNEGN6RXGQZ","created_at":"2026-07-05T01:01:35.016558+00:00"},{"alias_kind":"pith_short_8","alias_value":"TPWABYNE","created_at":"2026-07-05T01:01:35.016558+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29664","citing_title":"Benchmarking Geospatial Foundation Models for Agriculture Applications","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY","json":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY.json","graph_json":"https://pith.science/api/pith-number/TPWABYNEGN6RXGQZNR3ICRF2XY/graph.json","events_json":"https://pith.science/api/pith-number/TPWABYNEGN6RXGQZNR3ICRF2XY/events.json","paper":"https://pith.science/paper/TPWABYNE"},"agent_actions":{"view_html":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY","download_json":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY.json","view_paper":"https://pith.science/paper/TPWABYNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.11893&json=true","fetch_graph":"https://pith.science/api/pith-number/TPWABYNEGN6RXGQZNR3ICRF2XY/graph.json","fetch_events":"https://pith.science/api/pith-number/TPWABYNEGN6RXGQZNR3ICRF2XY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY/action/storage_attestation","attest_author":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY/action/author_attestation","sign_citation":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY/action/citation_signature","submit_replication":"https://pith.science/pith/TPWABYNEGN6RXGQZNR3ICRF2XY/action/replication_record"}},"created_at":"2026-07-05T01:01:35.016558+00:00","updated_at":"2026-07-05T01:01:35.016558+00:00"}