{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5UOB37NZKD7G64FMJQUQZAHNTO","short_pith_number":"pith:5UOB37NZ","schema_version":"1.0","canonical_sha256":"ed1c1dfdb950fe6f70ac4c290c80ed9ba3079727ff8bf14b95303b9724adc584","source":{"kind":"arxiv","id":"2507.08683","version":1},"attestation_state":"computed","paper":{"title":"MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aditi Golder, Debashis Gupta, Fan Yang, Kangning Cui, Ovidiu Csillik, Rongkhun Zhu, Sarra Alaqahtani, V. Paul Pauca, Wei Tang","submitted_at":"2025-07-11T15:33:51Z","abstract_excerpt":"Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrinsic similarities and differences among data samples has led to state-of-the-art results in computer vision tasks. These strengths make CL particularly well-suited for Earth System Observation (ESO), where diverse satellite modalities such as optical and SAR imagery offer naturally aligned views of the same geospatial regions. However, ESO presents unique challenges, including high inter-class similarity, scene clutte"},"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":"2507.08683","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-11T15:33:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8f62cf4624730286b198b6ddb7d0337674a8e081696ead23307e3f9f286113dd","abstract_canon_sha256":"a6b50782e595e8fc47e178d3ede7d54fbda5d9e34c67a68003fe8b5a0150384e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:44.782460Z","signature_b64":"apYWyEtMqMDizwc/uM5uoawdwRc5b3j2yFGTKCteJuI3/O7RHUlkKtzdZ59H2TuX+IeYfqOjPdBoFs9kTb2WCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed1c1dfdb950fe6f70ac4c290c80ed9ba3079727ff8bf14b95303b9724adc584","last_reissued_at":"2026-07-05T11:35:44.781846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:44.781846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aditi Golder, Debashis Gupta, Fan Yang, Kangning Cui, Ovidiu Csillik, Rongkhun Zhu, Sarra Alaqahtani, V. Paul Pauca, Wei Tang","submitted_at":"2025-07-11T15:33:51Z","abstract_excerpt":"Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrinsic similarities and differences among data samples has led to state-of-the-art results in computer vision tasks. These strengths make CL particularly well-suited for Earth System Observation (ESO), where diverse satellite modalities such as optical and SAR imagery offer naturally aligned views of the same geospatial regions. However, ESO presents unique challenges, including high inter-class similarity, scene clutte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08683","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/2507.08683/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":"2507.08683","created_at":"2026-07-05T11:35:44.781932+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.08683v1","created_at":"2026-07-05T11:35:44.781932+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08683","created_at":"2026-07-05T11:35:44.781932+00:00"},{"alias_kind":"pith_short_12","alias_value":"5UOB37NZKD7G","created_at":"2026-07-05T11:35:44.781932+00:00"},{"alias_kind":"pith_short_16","alias_value":"5UOB37NZKD7G64FM","created_at":"2026-07-05T11:35:44.781932+00:00"},{"alias_kind":"pith_short_8","alias_value":"5UOB37NZ","created_at":"2026-07-05T11:35:44.781932+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15397","citing_title":"ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2510.18935","citing_title":"Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications","ref_index":117,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO","json":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO.json","graph_json":"https://pith.science/api/pith-number/5UOB37NZKD7G64FMJQUQZAHNTO/graph.json","events_json":"https://pith.science/api/pith-number/5UOB37NZKD7G64FMJQUQZAHNTO/events.json","paper":"https://pith.science/paper/5UOB37NZ"},"agent_actions":{"view_html":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO","download_json":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO.json","view_paper":"https://pith.science/paper/5UOB37NZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.08683&json=true","fetch_graph":"https://pith.science/api/pith-number/5UOB37NZKD7G64FMJQUQZAHNTO/graph.json","fetch_events":"https://pith.science/api/pith-number/5UOB37NZKD7G64FMJQUQZAHNTO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO/action/storage_attestation","attest_author":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO/action/author_attestation","sign_citation":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO/action/citation_signature","submit_replication":"https://pith.science/pith/5UOB37NZKD7G64FMJQUQZAHNTO/action/replication_record"}},"created_at":"2026-07-05T11:35:44.781932+00:00","updated_at":"2026-07-05T11:35:44.781932+00:00"}