{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FAHOENW3NDWRR7MDKE3LUDSJTU","short_pith_number":"pith:FAHOENW3","schema_version":"1.0","canonical_sha256":"280ee236db68ed18fd835136ba0e499d2537d1f1de08610baba82dcf2f727ccd","source":{"kind":"arxiv","id":"2406.18295","version":1},"attestation_state":"computed","paper":{"title":"Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Luyts, Bertrand Le Saux, Casper Fibaek, Jente Bosmans, Luke Camilleri, Nikolaos Dionelis","submitted_at":"2024-06-26T12:27:06Z","abstract_excerpt":"When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should for most cases be used rather than problem-specific models. We focus on the specific Computer Vision application of Foundation Models for Earth Observation (EO) and geospatial AI. These models can solve important problems we are tackling, including for example land cover classification, crop type mapping, flood segmentation, building density estimation, and road regression segmentation. In this paper, we show that for "},"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":"2406.18295","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-26T12:27:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"45acfbf98642a89be58a9d01d21a65e1b73f94a6ed5e34201217b752112f2ad1","abstract_canon_sha256":"0a5e631874b82d0a6344bd4bc39114c2fee3185a1089cbe3f0aff3522de98af6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:58.476358Z","signature_b64":"orioDJUAxL6VotHIR94ysX59saBOGFIb+cpzNt2dzqAETsWZR1EpxehBi4wbBe8K2KVVfwxWDK5uhM1X2655AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"280ee236db68ed18fd835136ba0e499d2537d1f1de08610baba82dcf2f727ccd","last_reissued_at":"2026-07-05T08:36:58.475881Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:58.475881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Andreas Luyts, Bertrand Le Saux, Casper Fibaek, Jente Bosmans, Luke Camilleri, Nikolaos Dionelis","submitted_at":"2024-06-26T12:27:06Z","abstract_excerpt":"When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should for most cases be used rather than problem-specific models. We focus on the specific Computer Vision application of Foundation Models for Earth Observation (EO) and geospatial AI. These models can solve important problems we are tackling, including for example land cover classification, crop type mapping, flood segmentation, building density estimation, and road regression segmentation. In this paper, we show that for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18295","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.18295/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":"2406.18295","created_at":"2026-07-05T08:36:58.475940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.18295v1","created_at":"2026-07-05T08:36:58.475940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18295","created_at":"2026-07-05T08:36:58.475940+00:00"},{"alias_kind":"pith_short_12","alias_value":"FAHOENW3NDWR","created_at":"2026-07-05T08:36:58.475940+00:00"},{"alias_kind":"pith_short_16","alias_value":"FAHOENW3NDWRR7MD","created_at":"2026-07-05T08:36:58.475940+00:00"},{"alias_kind":"pith_short_8","alias_value":"FAHOENW3","created_at":"2026-07-05T08:36:58.475940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24120","citing_title":"Flood Mapping from RGB imagery using a Vision Foundation Model","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU","json":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU.json","graph_json":"https://pith.science/api/pith-number/FAHOENW3NDWRR7MDKE3LUDSJTU/graph.json","events_json":"https://pith.science/api/pith-number/FAHOENW3NDWRR7MDKE3LUDSJTU/events.json","paper":"https://pith.science/paper/FAHOENW3"},"agent_actions":{"view_html":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU","download_json":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU.json","view_paper":"https://pith.science/paper/FAHOENW3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.18295&json=true","fetch_graph":"https://pith.science/api/pith-number/FAHOENW3NDWRR7MDKE3LUDSJTU/graph.json","fetch_events":"https://pith.science/api/pith-number/FAHOENW3NDWRR7MDKE3LUDSJTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU/action/storage_attestation","attest_author":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU/action/author_attestation","sign_citation":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU/action/citation_signature","submit_replication":"https://pith.science/pith/FAHOENW3NDWRR7MDKE3LUDSJTU/action/replication_record"}},"created_at":"2026-07-05T08:36:58.475940+00:00","updated_at":"2026-07-05T08:36:58.475940+00:00"}