{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5TXOJLRLQNEJUMOWA74VUZ2PL2","short_pith_number":"pith:5TXOJLRL","schema_version":"1.0","canonical_sha256":"eceee4ae2b83489a31d607f95a674f5e9ce6f163f4c191a7de96c02d7dce90f1","source":{"kind":"arxiv","id":"2207.08051","version":3},"attestation_state":"computed","paper":{"title":"SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenlin Meng, David B. Lobell, Erik Rozi, Marshall Burke, Patrick Liu, Samar Khanna, Stefano Ermon, Yezhen Cong, Yutong He","submitted_at":"2022-07-17T01:35:29Z","abstract_excerpt":"Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to further improve existing pre-training strategies. In this paper, we present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage temporal information, we include a temporal embedding along with independentl"},"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":"2207.08051","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-17T01:35:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c07a200e1ed62ce67ac40460cbccf0e308bad26ddc2ea0f591fe581f623f4744","abstract_canon_sha256":"581297da5495fd772a73285b62fac271e062f6f59f32a7f9a47faf7c0366f677"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:33:14.476759Z","signature_b64":"6zLpyvNZb5b+8gkFiO+x3Q5LznqcUz4Y1004wUJBJPdILIR+t4gbYYvmrYn5N6qQ5ha6Vjc+ujpOK7H4LjswCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eceee4ae2b83489a31d607f95a674f5e9ce6f163f4c191a7de96c02d7dce90f1","last_reissued_at":"2026-07-05T05:33:14.476230Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:33:14.476230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Chenlin Meng, David B. Lobell, Erik Rozi, Marshall Burke, Patrick Liu, Samar Khanna, Stefano Ermon, Yezhen Cong, Yutong He","submitted_at":"2022-07-17T01:35:29Z","abstract_excerpt":"Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to further improve existing pre-training strategies. In this paper, we present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage temporal information, we include a temporal embedding along with independentl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.08051","kind":"arxiv","version":3},"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/2207.08051/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":"2207.08051","created_at":"2026-07-05T05:33:14.476287+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.08051v3","created_at":"2026-07-05T05:33:14.476287+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.08051","created_at":"2026-07-05T05:33:14.476287+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TXOJLRLQNEJ","created_at":"2026-07-05T05:33:14.476287+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TXOJLRLQNEJUMOW","created_at":"2026-07-05T05:33:14.476287+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TXOJLRL","created_at":"2026-07-05T05:33:14.476287+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17242","citing_title":"Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12595","citing_title":"Emerging Flexible Designs for Geospatial Multimodal Foundation Models","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28174","citing_title":"FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09787","citing_title":"Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08171","citing_title":"OceanMAE: A Foundation Model for Ocean Remote Sensing","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21032","citing_title":"Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2","json":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2.json","graph_json":"https://pith.science/api/pith-number/5TXOJLRLQNEJUMOWA74VUZ2PL2/graph.json","events_json":"https://pith.science/api/pith-number/5TXOJLRLQNEJUMOWA74VUZ2PL2/events.json","paper":"https://pith.science/paper/5TXOJLRL"},"agent_actions":{"view_html":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2","download_json":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2.json","view_paper":"https://pith.science/paper/5TXOJLRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.08051&json=true","fetch_graph":"https://pith.science/api/pith-number/5TXOJLRLQNEJUMOWA74VUZ2PL2/graph.json","fetch_events":"https://pith.science/api/pith-number/5TXOJLRLQNEJUMOWA74VUZ2PL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2/action/storage_attestation","attest_author":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2/action/author_attestation","sign_citation":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2/action/citation_signature","submit_replication":"https://pith.science/pith/5TXOJLRLQNEJUMOWA74VUZ2PL2/action/replication_record"}},"created_at":"2026-07-05T05:33:14.476287+00:00","updated_at":"2026-07-05T05:33:14.476287+00:00"}